Covail
T cell data
mypairs(dat_mapped[,c(glue("Day15{c(rbind(S1[1:2], S2[1:2]))}"))], gap=0)
tab=mysapply (1:length(S1), function(i) {
c(mytable(!is.na(dat_mapped[[glue("Day15{S1[i]}")]]), !is.na(dat_mapped[[glue("Day15{S2[i]}")]])))
})
rownames(tab)=sub(".S1", "", S1)
tab## [,1] [,2] [,3] [,4]
## cd4_IFNg.IL2_COV2.CON 686 0 0 576
## cd4_IFNg.IL2_BA.4.5 691 12 2 557
## cd8_IFNg.IL2_COV2.CON 686 0 1 575
## cd8_IFNg.IL2_BA.4.5 691 12 4 555
## cd4_IFNg.IL2.154_COV2.CON 686 0 0 576
## cd4_IFNg.IL2.154_BA.4.5 691 12 2 557
## cd4_IL4.IL5.IL13.154_COV2.CON 686 0 0 576
## cd4_IL4.IL5.IL13.154_BA.4.5 691 12 2 557
## cd4_IL21_COV2.CON 686 0 0 576
## cd4_IL21_BA.4.5 691 12 2 557
## cd4_IFNg_COV2.CON 686 0 0 576
## cd4_IFNg_BA.4.5 691 12 2 557
## cd4_IL2_COV2.CON 686 0 0 576
## cd4_IL2_BA.4.5 691 12 2 557
## cd4_TNFa_COV2.CON 686 0 0 576
## cd4_TNFa_BA.4.5 691 12 2 557
## cd8_IFNg_COV2.CON 686 0 1 575
## cd8_IFNg_BA.4.5 691 12 4 555
## cd8_IL2_COV2.CON 686 0 1 575
## cd8_IL2_BA.4.5 691 12 4 555
## cd8_TNFa_COV2.CON 686 0 1 575
## cd8_TNFa_BA.4.5 691 12 4 555
## cd4_154_COV2.CON 686 0 0 576
## cd4_154_BA.4.5 691 12 2 557
## cd4_IL17a_COV2.CON 686 0 0 576
## cd4_IL17a_BA.4.5 691 12 2 557
## cd4_IL4.154_COV2.CON 686 0 0 576
## cd4_IL4.154_BA.4.5 691 12 2 557
## cd4_IL5.IL13.154_COV2.CON 686 0 0 576
## cd4_IL5.IL13.154_BA.4.5 691 12 2 557
## cd4_CXCR5.154_COV2.CON 686 0 1 575
## cd4_CXCR5.154_BA.4.5 694 12 7 549
## cd4_CXCR5.IL21_COV2.CON 686 0 1 575
## cd4_CXCR5.IL21_BA.4.5 694 12 7 549
## cd8_IFNg.IL2.TNFa_COV2.CON 686 0 1 575
## cd8_IFNg.IL2.TNFa_BA.4.5 691 12 4 555i=2; mytable(!is.na(dat_mapped[[glue("Day15{S1[i]}")]]), !is.na(dat_mapped[[glue("Day15{S2[i]}")]]))##
## FALSE TRUE
## FALSE 691 2
## TRUE 12 557i=1; mytable(!is.na(dat_mapped[[glue("Day15{S1[i]}")]]), !is.na(dat_mapped[[glue("Day15{S2[i]}")]]), dat_mapped$COVIDIndD22toD91)## , , = 0
##
##
## FALSE TRUE
## FALSE 598 0
## TRUE 0 456
##
## , , = 1
##
##
## FALSE TRUE
## FALSE 55 0
## TRUE 0 107
##
## , , = NA
##
##
## FALSE TRUE
## FALSE 33 0
## TRUE 0 13i=1; mytable(!is.na(dat_mapped[[glue("Day15{S2[i]}")]]), !is.na(dat_mapped[["Day15cd4_IFNg.IL2_Wuhan.N"]]),
dat_mapped$COVIDIndD22toD91)## , , = 0
##
##
## FALSE TRUE
## FALSE 598 0
## TRUE 26 430
##
## , , = 1
##
##
## FALSE TRUE
## FALSE 55 0
## TRUE 5 102
##
## , , = NA
##
##
## FALSE TRUE
## FALSE 33 0
## TRUE 0 13mytable(rowSums(!is.na(dat_mapped[glue("Day15{tcellvv}")])))##
## 0 36 54 72 85 86 88 90
## 686 5 10 20 5 3 12 521# t(dat_mapped[which(rowSums(!is.na(dat_mapped[glue("Day15{tcellvv}")]))==36),201:300])mypairs(dat_mapped[,c(glue("B{c(rbind(S1[1:2], S2[1:2]))}"))], gap=0)
tab=mysapply (1:length(S1), function(i) {
c(mytable(!is.na(dat_mapped[[glue("B{S1[i]}")]]), !is.na(dat_mapped[[glue("B{S2[i]}")]])))
})
rownames(tab)=sub(".S1", "", S1)
tab## [,1] [,2] [,3] [,4]
## cd4_IFNg.IL2_COV2.CON 686 0 0 576
## cd4_IFNg.IL2_BA.4.5 691 19 1 551
## cd8_IFNg.IL2_COV2.CON 686 0 0 576
## cd8_IFNg.IL2_BA.4.5 691 19 3 549
## cd4_IFNg.IL2.154_COV2.CON 686 0 0 576
## cd4_IFNg.IL2.154_BA.4.5 691 19 1 551
## cd4_IL4.IL5.IL13.154_COV2.CON 686 0 0 576
## cd4_IL4.IL5.IL13.154_BA.4.5 691 19 1 551
## cd4_IL21_COV2.CON 686 0 0 576
## cd4_IL21_BA.4.5 691 19 1 551
## cd4_IFNg_COV2.CON 686 0 0 576
## cd4_IFNg_BA.4.5 691 19 1 551
## cd4_IL2_COV2.CON 686 0 0 576
## cd4_IL2_BA.4.5 691 19 1 551
## cd4_TNFa_COV2.CON 686 0 0 576
## cd4_TNFa_BA.4.5 691 19 1 551
## cd8_IFNg_COV2.CON 686 0 0 576
## cd8_IFNg_BA.4.5 691 19 3 549
## cd8_IL2_COV2.CON 686 0 0 576
## cd8_IL2_BA.4.5 691 19 3 549
## cd8_TNFa_COV2.CON 686 0 0 576
## cd8_TNFa_BA.4.5 691 19 3 549
## cd4_154_COV2.CON 686 0 0 576
## cd4_154_BA.4.5 691 19 1 551
## cd4_IL17a_COV2.CON 686 0 0 576
## cd4_IL17a_BA.4.5 691 19 1 551
## cd4_IL4.154_COV2.CON 686 0 0 576
## cd4_IL4.154_BA.4.5 691 19 1 551
## cd4_IL5.IL13.154_COV2.CON 686 0 0 576
## cd4_IL5.IL13.154_BA.4.5 691 19 1 551
## cd4_CXCR5.154_COV2.CON 686 0 1 575
## cd4_CXCR5.154_BA.4.5 694 19 6 543
## cd4_CXCR5.IL21_COV2.CON 686 0 1 575
## cd4_CXCR5.IL21_BA.4.5 694 19 6 543
## cd8_IFNg.IL2.TNFa_COV2.CON 686 0 0 576
## cd8_IFNg.IL2.TNFa_BA.4.5 691 19 3 549i=2; mytable(!is.na(dat_mapped[[glue("B{S1[i]}")]]), !is.na(dat_mapped[[glue("B{S2[i]}")]]))##
## FALSE TRUE
## FALSE 691 1
## TRUE 19 551i=1; mytable(!is.na(dat_mapped[[glue("B{S1[i]}")]]), !is.na(dat_mapped[[glue("B{S2[i]}")]]), dat_mapped$COVIDIndD22toD91)## , , = 0
##
##
## FALSE TRUE
## FALSE 598 0
## TRUE 0 456
##
## , , = 1
##
##
## FALSE TRUE
## FALSE 54 0
## TRUE 0 108
##
## , , = NA
##
##
## FALSE TRUE
## FALSE 34 0
## TRUE 0 12i=1; mytable(!is.na(dat_mapped[[glue("B{S2[i]}")]]), !is.na(dat_mapped[["Day15cd4_IFNg.IL2_Wuhan.N"]]),
dat_mapped$COVIDIndD22toD91)## , , = 0
##
##
## FALSE TRUE
## FALSE 592 6
## TRUE 32 424
##
## , , = 1
##
##
## FALSE TRUE
## FALSE 54 0
## TRUE 6 102
##
## , , = NA
##
##
## FALSE TRUE
## FALSE 33 1
## TRUE 0 12mytable(rowSums(!is.na(dat_mapped[glue("B{tcellvv}")])))##
## 0 36 54 72 85 86 88 90
## 686 5 18 38 3 3 11 498t(dat_mapped[which(rowSums(!is.na(dat_mapped[glue("B{tcellvv}")]))==36),201:300])## 188 306 541
## Bcd4_CXCR5.IL21_BA.4.5.S2 NA NA NA
## Bcd4_CXCR5.IL21_COV2.CON.S1 0.014437157 0.004467056 0.001000000
## Bcd4_CXCR5.IL21_COV2.CON.S2 0.001000000 0.001000000 0.001000000
## Bcd4_CXCR5.IL21_Wuhan.N NA NA NA
## Bcd4_IFNg_BA.4.5.S1 NA NA NA
## Bcd4_IFNg_BA.4.5.S2 NA NA NA
## Bcd4_IFNg_COV2.CON.S1 0.231073038 0.049872446 0.089807106
## Bcd4_IFNg_COV2.CON.S2 0.131226468 0.043148201 0.103975807
## Bcd4_IFNg_Wuhan.N NA NA NA
## Bcd4_IFNg.IL2_BA.4.5.S1 NA NA NA
## Bcd4_IFNg.IL2_BA.4.5.S2 NA NA NA
## Bcd4_IFNg.IL2_COV2.CON.S1 0.368800537 0.071189236 0.082697693
## Bcd4_IFNg.IL2_COV2.CON.S2 0.238105378 0.065693293 0.099041092
## Bcd4_IFNg.IL2_Wuhan.N NA NA NA
## Bcd4_IFNg.IL2.154_BA.4.5.S1 NA NA NA
## Bcd4_IFNg.IL2.154_BA.4.5.S2 NA NA NA
## Bcd4_IFNg.IL2.154_COV2.CON.S1 0.418913129 0.074412217 0.082763326
## Bcd4_IFNg.IL2.154_COV2.CON.S2 0.264305574 0.076316213 0.100689069
## Bcd4_IFNg.IL2.154_Wuhan.N NA NA NA
## Bcd4_IL17a_BA.4.5.S1 NA NA NA
## Bcd4_IL17a_BA.4.5.S2 NA NA NA
## Bcd4_IL17a_COV2.CON.S1 0.004124902 0.001000000 0.004358710
## Bcd4_IL17a_COV2.CON.S2 0.003980733 0.003144674 0.010688086
## Bcd4_IL17a_Wuhan.N NA NA NA
## Bcd4_IL2_BA.4.5.S1 NA NA NA
## Bcd4_IL2_BA.4.5.S2 NA NA NA
## Bcd4_IL2_COV2.CON.S1 0.269267644 0.063424004 0.038801777
## Bcd4_IL2_COV2.CON.S2 0.185892686 0.061919684 0.050058636
## Bcd4_IL2_Wuhan.N NA NA NA
## Bcd4_IL21_BA.4.5.S1 NA NA NA
## Bcd4_IL21_BA.4.5.S2 NA NA NA
## Bcd4_IL21_COV2.CON.S1 0.076389212 0.007690037 0.050302716
## Bcd4_IL21_COV2.CON.S2 0.009814104 0.003759814 0.054059757
## Bcd4_IL21_Wuhan.N NA NA NA
## Bcd4_IL4.154_BA.4.5.S1 NA NA NA
## Bcd4_IL4.154_BA.4.5.S2 NA NA NA
## Bcd4_IL4.154_COV2.CON.S1 0.001000000 0.001000000 0.001000000
## Bcd4_IL4.154_COV2.CON.S2 0.001000000 0.001000000 0.001000000
## Bcd4_IL4.154_Wuhan.N NA NA NA
## Bcd4_IL4.IL5.IL13.154_BA.4.5.S1 NA NA NA
## Bcd4_IL4.IL5.IL13.154_BA.4.5.S2 NA NA NA
## Bcd4_IL4.IL5.IL13.154_COV2.CON.S1 0.001000000 0.001903710 0.001000000
## Bcd4_IL4.IL5.IL13.154_COV2.CON.S2 0.001000000 0.001873010 0.001000000
## Bcd4_IL4.IL5.IL13.154_Wuhan.N NA NA NA
## Bcd4_IL5.IL13.154_BA.4.5.S1 NA NA NA
## Bcd4_IL5.IL13.154_BA.4.5.S2 NA NA NA
## Bcd4_IL5.IL13.154_COV2.CON.S1 0.001000000 0.001903710 0.001000000
## Bcd4_IL5.IL13.154_COV2.CON.S2 0.001990367 0.001873010 0.001000000
## Bcd4_IL5.IL13.154_Wuhan.N NA NA NA
## Bcd4_TNFa_BA.4.5.S1 NA NA NA
## Bcd4_TNFa_BA.4.5.S2 NA NA NA
## Bcd4_TNFa_COV2.CON.S1 0.355526818 0.001000000 0.001000000
## Bcd4_TNFa_COV2.CON.S2 0.253389639 0.001000000 0.001000000
## Bcd4_TNFa_Wuhan.N NA NA NA
## Bcd8_IFNg_BA.4.5.S1 NA NA NA
## Bcd8_IFNg_BA.4.5.S2 NA NA NA
## Bcd8_IFNg_COV2.CON.S1 0.004075968 0.001000000 0.017488870
## Bcd8_IFNg_COV2.CON.S2 0.006330431 0.003894445 0.001000000
## Bcd8_IFNg_Wuhan.N NA NA NA
## Bcd8_IFNg.IL2_BA.4.5.S1 NA NA NA
## Bcd8_IFNg.IL2_BA.4.5.S2 NA NA NA
## Bcd8_IFNg.IL2_COV2.CON.S1 0.004075968 0.001000000 0.033458209
## Bcd8_IFNg.IL2_COV2.CON.S2 0.008633358 0.003894445 0.012971951
## Bcd8_IFNg.IL2_Wuhan.N NA NA NA
## Bcd8_IFNg.IL2.TNFa_BA.4.5.S1 NA NA NA
## Bcd8_IFNg.IL2.TNFa_BA.4.5.S2 NA NA NA
## Bcd8_IFNg.IL2.TNFa_COV2.CON.S1 0.010023720 0.001000000 0.005209820
## Bcd8_IFNg.IL2.TNFa_COV2.CON.S2 0.014532644 0.005627565 0.001000000
## Bcd8_IFNg.IL2.TNFa_Wuhan.N NA NA NA
## Bcd8_IL2_BA.4.5.S1 NA NA NA
## Bcd8_IL2_BA.4.5.S2 NA NA NA
## Bcd8_IL2_COV2.CON.S1 0.001000000 0.001000000 0.008201745
## Bcd8_IL2_COV2.CON.S2 0.002302927 0.001000000 0.029646354
## Bcd8_IL2_Wuhan.N NA NA NA
## Bcd8_TNFa_BA.4.5.S1 NA NA NA
## Bcd8_TNFa_BA.4.5.S2 NA NA NA
## Bcd8_TNFa_COV2.CON.S1 0.005947751 0.001000000 0.012760338
## Bcd8_TNFa_COV2.CON.S2 0.008202214 0.001000000 0.001000000
## Bcd8_TNFa_Wuhan.N NA NA NA
## Day15cd4_154_BA.4.5.S1 0.670446287 0.138362130 0.093446034
## Day15cd4_154_BA.4.5.S2 0.441614813 NA 0.074328671
## Day15cd4_154_COV2.CON.S1 0.732183666 0.180064972 0.128435391
## Day15cd4_154_COV2.CON.S2 0.424804333 0.193484068 0.089379914
## Day15cd4_154_Wuhan.N 0.039243080 NA 0.073720925
## Day15cd4_CXCR5.154_BA.4.5.S1 0.038345029 0.018148994 0.004033772
## Day15cd4_CXCR5.154_BA.4.5.S2 0.023400806 NA 0.006143236
## Day15cd4_CXCR5.154_COV2.CON.S1 0.032358148 0.033012250 0.006973130
## Day15cd4_CXCR5.154_COV2.CON.S2 0.008763167 0.024092413 0.008965998
## Day15cd4_CXCR5.154_Wuhan.N 0.006753563 NA 0.011745836
## Day15cd4_CXCR5.IL21_BA.4.5.S1 0.021089766 0.002865631 0.001000000
## Day15cd4_CXCR5.IL21_BA.4.5.S2 0.011700403 NA 0.002546184
## Day15cd4_CXCR5.IL21_COV2.CON.S1 0.013323943 0.007074054 0.001086433
## Day15cd4_CXCR5.IL21_COV2.CON.S2 0.003505267 0.010144174 0.001721183
## Day15cd4_CXCR5.IL21_Wuhan.N 0.001688391 NA 0.001743479
## Day15cd4_IFNg_BA.4.5.S1 0.568500577 0.112581012 0.097479806
## Day15cd4_IFNg_BA.4.5.S2 0.346254485 NA 0.071617099
## Day15cd4_IFNg_COV2.CON.S1 0.651922454 0.146838480 0.126505564
## Day15cd4_IFNg_COV2.CON.S2 0.333500633 0.171624443 0.097445311
## Day15cd4_IFNg_Wuhan.N 0.042517337 NA 0.065530917
## Day15cd4_IFNg.IL2_BA.4.5.S1 0.710377183 0.151553499 0.096430692
## 542 546
## Bcd4_CXCR5.IL21_BA.4.5.S2 NA NA
## Bcd4_CXCR5.IL21_COV2.CON.S1 0.003822271 0.001000000
## Bcd4_CXCR5.IL21_COV2.CON.S2 0.001000000 0.001000000
## Bcd4_CXCR5.IL21_Wuhan.N NA NA
## Bcd4_IFNg_BA.4.5.S1 NA NA
## Bcd4_IFNg_BA.4.5.S2 NA NA
## Bcd4_IFNg_COV2.CON.S1 0.069755310 0.030218373
## Bcd4_IFNg_COV2.CON.S2 0.125680722 0.039321171
## Bcd4_IFNg_Wuhan.N NA NA
## Bcd4_IFNg.IL2_BA.4.5.S1 NA NA
## Bcd4_IFNg.IL2_BA.4.5.S2 NA NA
## Bcd4_IFNg.IL2_COV2.CON.S1 0.116576996 0.023255226
## Bcd4_IFNg.IL2_COV2.CON.S2 0.224547861 0.051406264
## Bcd4_IFNg.IL2_Wuhan.N NA NA
## Bcd4_IFNg.IL2.154_BA.4.5.S1 NA NA
## Bcd4_IFNg.IL2.154_BA.4.5.S2 NA NA
## Bcd4_IFNg.IL2.154_COV2.CON.S1 0.129272234 0.025418511
## Bcd4_IFNg.IL2.154_COV2.CON.S2 0.233485722 0.051406264
## Bcd4_IFNg.IL2.154_Wuhan.N NA NA
## Bcd4_IL17a_BA.4.5.S1 NA NA
## Bcd4_IL17a_BA.4.5.S2 NA NA
## Bcd4_IL17a_COV2.CON.S1 0.001000000 0.002095672
## Bcd4_IL17a_COV2.CON.S2 0.002012079 0.001000000
## Bcd4_IL17a_Wuhan.N NA NA
## Bcd4_IL2_BA.4.5.S1 NA NA
## Bcd4_IL2_BA.4.5.S2 NA NA
## Bcd4_IL2_COV2.CON.S1 0.108898207 0.012506416
## Bcd4_IL2_COV2.CON.S2 0.188351643 0.043743813
## Bcd4_IL2_Wuhan.N NA NA
## Bcd4_IL21_BA.4.5.S1 NA NA
## Bcd4_IL21_BA.4.5.S2 NA NA
## Bcd4_IL21_COV2.CON.S1 0.003073321 0.001000000
## Bcd4_IL21_COV2.CON.S2 0.003134613 0.001000000
## Bcd4_IL21_Wuhan.N NA NA
## Bcd4_IL4.154_BA.4.5.S1 NA NA
## Bcd4_IL4.154_BA.4.5.S2 NA NA
## Bcd4_IL4.154_COV2.CON.S1 0.001000000 0.001000000
## Bcd4_IL4.154_COV2.CON.S2 0.001000000 0.001978670
## Bcd4_IL4.154_Wuhan.N NA NA
## Bcd4_IL4.IL5.IL13.154_BA.4.5.S1 NA NA
## Bcd4_IL4.IL5.IL13.154_BA.4.5.S2 NA NA
## Bcd4_IL4.IL5.IL13.154_COV2.CON.S1 0.001000000 0.001000000
## Bcd4_IL4.IL5.IL13.154_COV2.CON.S2 0.001000000 0.001000000
## Bcd4_IL4.IL5.IL13.154_Wuhan.N NA NA
## Bcd4_IL5.IL13.154_BA.4.5.S1 NA NA
## Bcd4_IL5.IL13.154_BA.4.5.S2 NA NA
## Bcd4_IL5.IL13.154_COV2.CON.S1 0.001000000 0.001000000
## Bcd4_IL5.IL13.154_COV2.CON.S2 0.001000000 0.001000000
## Bcd4_IL5.IL13.154_Wuhan.N NA NA
## Bcd4_TNFa_BA.4.5.S1 NA NA
## Bcd4_TNFa_BA.4.5.S2 NA NA
## Bcd4_TNFa_COV2.CON.S1 0.087647264 0.001000000
## Bcd4_TNFa_COV2.CON.S2 0.169840571 0.001000000
## Bcd4_TNFa_Wuhan.N NA NA
## Bcd8_IFNg_BA.4.5.S1 NA NA
## Bcd8_IFNg_BA.4.5.S2 NA NA
## Bcd8_IFNg_COV2.CON.S1 0.025910547 0.019815029
## Bcd8_IFNg_COV2.CON.S2 0.145304185 0.032749356
## Bcd8_IFNg_Wuhan.N NA NA
## Bcd8_IFNg.IL2_BA.4.5.S1 NA NA
## Bcd8_IFNg.IL2_BA.4.5.S2 NA NA
## Bcd8_IFNg.IL2_COV2.CON.S1 0.022343196 0.016886600
## Bcd8_IFNg.IL2_COV2.CON.S2 0.159070861 0.029820927
## Bcd8_IFNg.IL2_Wuhan.N NA NA
## Bcd8_IFNg.IL2.TNFa_BA.4.5.S1 NA NA
## Bcd8_IFNg.IL2.TNFa_BA.4.5.S2 NA NA
## Bcd8_IFNg.IL2.TNFa_COV2.CON.S1 0.022718830 0.022563465
## Bcd8_IFNg.IL2.TNFa_COV2.CON.S2 0.144801454 0.029182500
## Bcd8_IFNg.IL2.TNFa_Wuhan.N NA NA
## Bcd8_IL2_BA.4.5.S1 NA NA
## Bcd8_IL2_BA.4.5.S2 NA NA
## Bcd8_IL2_COV2.CON.S1 0.003849077 0.001000000
## Bcd8_IL2_COV2.CON.S2 0.096869457 0.001000000
## Bcd8_IL2_Wuhan.N NA NA
## Bcd8_TNFa_BA.4.5.S1 NA NA
## Bcd8_TNFa_BA.4.5.S2 NA NA
## Bcd8_TNFa_COV2.CON.S1 0.022718830 0.028672314
## Bcd8_TNFa_COV2.CON.S2 0.127467427 0.017519679
## Bcd8_TNFa_Wuhan.N NA NA
## Day15cd4_154_BA.4.5.S1 0.099916638 0.101356502
## Day15cd4_154_BA.4.5.S2 0.246679127 0.089917283
## Day15cd4_154_COV2.CON.S1 0.143497231 0.083196271
## Day15cd4_154_COV2.CON.S2 0.236429577 0.100542189
## Day15cd4_154_Wuhan.N 0.001000000 0.011245048
## Day15cd4_CXCR5.154_BA.4.5.S1 0.012290861 0.001911096
## Day15cd4_CXCR5.154_BA.4.5.S2 0.043487000 0.003336726
## Day15cd4_CXCR5.154_COV2.CON.S1 0.017738435 0.003865407
## Day15cd4_CXCR5.154_COV2.CON.S2 0.036133347 0.005295769
## Day15cd4_CXCR5.154_Wuhan.N 0.001000000 0.001000000
## Day15cd4_CXCR5.IL21_BA.4.5.S1 0.001000000 0.005801711
## Day15cd4_CXCR5.IL21_BA.4.5.S2 0.003143008 0.003162416
## Day15cd4_CXCR5.IL21_COV2.CON.S1 0.005689719 0.001000000
## Day15cd4_CXCR5.IL21_COV2.CON.S2 0.009385890 0.001687840
## Day15cd4_CXCR5.IL21_Wuhan.N 0.002376547 0.001000000
## Day15cd4_IFNg_BA.4.5.S1 0.070479141 0.097465887
## Day15cd4_IFNg_BA.4.5.S2 0.175513422 0.105106859
## Day15cd4_IFNg_COV2.CON.S1 0.106430611 0.085038944
## Day15cd4_IFNg_COV2.CON.S2 0.194794945 0.125333192
## Day15cd4_IFNg_Wuhan.N 0.001000000 0.011451755
## Day15cd4_IFNg.IL2_BA.4.5.S1 0.104366912 0.110911981# cross-tabulate missingness between baseline and D15
tab=mysapply (1:length(S1), function(i) {
c(mytable(!is.na(dat_mapped[[glue("B{S1[i]}")]]), !is.na(dat_mapped[[glue("Day15{S1[i]}")]])))
})
rownames(tab)=sub(".S1", "", S1); tab## [,1] [,2] [,3] [,4]
## cd4_IFNg.IL2_COV2.CON 679 7 7 569
## cd4_IFNg.IL2_BA.4.5 679 14 13 556
## cd8_IFNg.IL2_COV2.CON 679 8 7 568
## cd8_IFNg.IL2_BA.4.5 681 14 13 554
## cd4_IFNg.IL2.154_COV2.CON 679 7 7 569
## cd4_IFNg.IL2.154_BA.4.5 679 14 13 556
## cd4_IL4.IL5.IL13.154_COV2.CON 679 7 7 569
## cd4_IL4.IL5.IL13.154_BA.4.5 679 14 13 556
## cd4_IL21_COV2.CON 679 7 7 569
## cd4_IL21_BA.4.5 679 14 13 556
## cd4_IFNg_COV2.CON 679 7 7 569
## cd4_IFNg_BA.4.5 679 14 13 556
## cd4_IL2_COV2.CON 679 7 7 569
## cd4_IL2_BA.4.5 679 14 13 556
## cd4_TNFa_COV2.CON 679 7 7 569
## cd4_TNFa_BA.4.5 679 14 13 556
## cd8_IFNg_COV2.CON 679 8 7 568
## cd8_IFNg_BA.4.5 681 14 13 554
## cd8_IL2_COV2.CON 679 8 7 568
## cd8_IL2_BA.4.5 681 14 13 554
## cd8_TNFa_COV2.CON 679 8 7 568
## cd8_TNFa_BA.4.5 681 14 13 554
## cd4_154_COV2.CON 679 7 7 569
## cd4_154_BA.4.5 679 14 13 556
## cd4_IL17a_COV2.CON 679 7 7 569
## cd4_IL17a_BA.4.5 679 14 13 556
## cd4_IL4.154_COV2.CON 679 7 7 569
## cd4_IL4.154_BA.4.5 679 14 13 556
## cd4_IL5.IL13.154_COV2.CON 679 7 7 569
## cd4_IL5.IL13.154_BA.4.5 679 14 13 556
## cd4_CXCR5.154_COV2.CON 680 7 7 568
## cd4_CXCR5.154_BA.4.5 687 14 13 548
## cd4_CXCR5.IL21_COV2.CON 680 7 7 568
## cd4_CXCR5.IL21_BA.4.5 687 14 13 548
## cd8_IFNg.IL2.TNFa_COV2.CON 679 8 7 568
## cd8_IFNg.IL2.TNFa_BA.4.5 681 14 13 554mypairs(dat_mapped[,c(glue("B{c(rbind(S1[1:2]))}"), glue("Day15{c(rbind(S1[1:2]))}"))], gap=0)
mypairs(log10(dat_mapped[,c(glue("Day91{c(rbind(S1[1:2], S2[1:2]))}"))]), gap=0)
tab=mysapply (1:length(S1), function(i) {
c(mytable(!is.na(dat_mapped[[glue("Day91{S1[i]}")]]), !is.na(dat_mapped[[glue("Day91{S2[i]}")]])))
})
rownames(tab)=sub(".S1", "", S1)
tab## [,1] [,2] [,3] [,4]
## cd4_IFNg.IL2_COV2.CON 1088 0 0 174
## cd4_IFNg.IL2_BA.4.5 1091 3 1 167
## cd8_IFNg.IL2_COV2.CON 1088 0 0 174
## cd8_IFNg.IL2_BA.4.5 1091 3 2 166
## cd4_IFNg.IL2.154_COV2.CON 1088 0 0 174
## cd4_IFNg.IL2.154_BA.4.5 1091 3 1 167
## cd4_IL4.IL5.IL13.154_COV2.CON 1088 0 0 174
## cd4_IL4.IL5.IL13.154_BA.4.5 1091 3 1 167
## cd4_IL21_COV2.CON 1088 0 0 174
## cd4_IL21_BA.4.5 1091 3 1 167
## cd4_IFNg_COV2.CON 1088 0 0 174
## cd4_IFNg_BA.4.5 1091 3 1 167
## cd4_IL2_COV2.CON 1088 0 0 174
## cd4_IL2_BA.4.5 1091 3 1 167
## cd4_TNFa_COV2.CON 1088 0 0 174
## cd4_TNFa_BA.4.5 1091 3 1 167
## cd8_IFNg_COV2.CON 1088 0 0 174
## cd8_IFNg_BA.4.5 1091 3 2 166
## cd8_IL2_COV2.CON 1088 0 0 174
## cd8_IL2_BA.4.5 1091 3 2 166
## cd8_TNFa_COV2.CON 1088 0 0 174
## cd8_TNFa_BA.4.5 1091 3 2 166
## cd4_154_COV2.CON 1088 0 0 174
## cd4_154_BA.4.5 1091 3 1 167
## cd4_IL17a_COV2.CON 1088 0 0 174
## cd4_IL17a_BA.4.5 1091 3 1 167
## cd4_IL4.154_COV2.CON 1088 0 0 174
## cd4_IL4.154_BA.4.5 1091 3 1 167
## cd4_IL5.IL13.154_COV2.CON 1088 0 0 174
## cd4_IL5.IL13.154_BA.4.5 1091 3 1 167
## cd4_CXCR5.154_COV2.CON 1088 0 0 174
## cd4_CXCR5.154_BA.4.5 1094 2 5 161
## cd4_CXCR5.IL21_COV2.CON 1088 0 0 174
## cd4_CXCR5.IL21_BA.4.5 1094 2 5 161
## cd8_IFNg.IL2.TNFa_COV2.CON 1088 0 0 174
## cd8_IFNg.IL2.TNFa_BA.4.5 1091 3 2 166i=2; mytable(!is.na(dat_mapped[[glue("B{S1[i]}")]]), !is.na(dat_mapped[[glue("B{S2[i]}")]]))##
## FALSE TRUE
## FALSE 691 1
## TRUE 19 551i=1; mytable(!is.na(dat_mapped[[glue("B{S1[i]}")]]), !is.na(dat_mapped[[glue("B{S2[i]}")]]), dat_mapped$COVIDIndD22toD91)## , , = 0
##
##
## FALSE TRUE
## FALSE 598 0
## TRUE 0 456
##
## , , = 1
##
##
## FALSE TRUE
## FALSE 54 0
## TRUE 0 108
##
## , , = NA
##
##
## FALSE TRUE
## FALSE 34 0
## TRUE 0 12i=1; mytable(!is.na(dat_mapped[[glue("B{S2[i]}")]]), !is.na(dat_mapped[["Day91cd4_IFNg.IL2_Wuhan.N"]]),
dat_mapped$COVIDIndD22toD91)## , , = 0
##
##
## FALSE TRUE
## FALSE 596 2
## TRUE 335 121
##
## , , = 1
##
##
## FALSE TRUE
## FALSE 54 0
## TRUE 77 31
##
## , , = NA
##
##
## FALSE TRUE
## FALSE 34 0
## TRUE 9 3mytable(rowSums(!is.na(dat_mapped[glue("B{tcellvv}")])))##
## 0 36 54 72 85 86 88 90
## 686 5 18 38 3 3 11 498# t(dat_mapped[which(rowSums(!is.na(dat_mapped[glue("B{tcellvv}")]))==36),201:300])
# cross-tabulate missingness between baseline and D91
tab=mysapply (1:length(S1), function(i) {
c(mytable(!is.na(dat_mapped[[glue("B{S1[i]}")]]), !is.na(dat_mapped[[glue("Day91{S1[i]}")]])))
})
rownames(tab)=sub(".S1", "", S1); tab## [,1] [,2] [,3] [,4]
## cd4_IFNg.IL2_COV2.CON 683 405 3 171
## cd4_IFNg.IL2_BA.4.5 688 404 4 166
## cd8_IFNg.IL2_COV2.CON 683 405 3 171
## cd8_IFNg.IL2_BA.4.5 690 403 4 165
## cd4_IFNg.IL2.154_COV2.CON 683 405 3 171
## cd4_IFNg.IL2.154_BA.4.5 688 404 4 166
## cd4_IL4.IL5.IL13.154_COV2.CON 683 405 3 171
## cd4_IL4.IL5.IL13.154_BA.4.5 688 404 4 166
## cd4_IL21_COV2.CON 683 405 3 171
## cd4_IL21_BA.4.5 688 404 4 166
## cd4_IFNg_COV2.CON 683 405 3 171
## cd4_IFNg_BA.4.5 688 404 4 166
## cd4_IL2_COV2.CON 683 405 3 171
## cd4_IL2_BA.4.5 688 404 4 166
## cd4_TNFa_COV2.CON 683 405 3 171
## cd4_TNFa_BA.4.5 688 404 4 166
## cd8_IFNg_COV2.CON 683 405 3 171
## cd8_IFNg_BA.4.5 690 403 4 165
## cd8_IL2_COV2.CON 683 405 3 171
## cd8_IL2_BA.4.5 690 403 4 165
## cd8_TNFa_COV2.CON 683 405 3 171
## cd8_TNFa_BA.4.5 690 403 4 165
## cd4_154_COV2.CON 683 405 3 171
## cd4_154_BA.4.5 688 404 4 166
## cd4_IL17a_COV2.CON 683 405 3 171
## cd4_IL17a_BA.4.5 688 404 4 166
## cd4_IL4.154_COV2.CON 683 405 3 171
## cd4_IL4.154_BA.4.5 688 404 4 166
## cd4_IL5.IL13.154_COV2.CON 683 405 3 171
## cd4_IL5.IL13.154_BA.4.5 688 404 4 166
## cd4_CXCR5.154_COV2.CON 683 405 4 170
## cd4_CXCR5.154_BA.4.5 696 403 4 159
## cd4_CXCR5.IL21_COV2.CON 683 405 4 170
## cd4_CXCR5.IL21_BA.4.5 696 403 4 159
## cd8_IFNg.IL2.TNFa_COV2.CON 683 405 3 171
## cd8_IFNg.IL2.TNFa_BA.4.5 690 403 4 165mypairs(log10(dat_mapped[,c(glue("B{c(rbind(S1[1:2]))}"), glue("Day91{c(rbind(S1[1:2]))}"))]), gap=0)
mypairs(log10(dat_mapped[,c(glue("Day181{c(rbind(S1[1:2], S2[1:2]))}"))]), gap=0)
tab=mysapply (1:length(S1), function(i) {
c(mytable(!is.na(dat_mapped[[glue("Day181{S1[i]}")]]), !is.na(dat_mapped[[glue("Day181{S2[i]}")]])))
})
rownames(tab)=sub(".S1", "", S1)
tab## [,1] [,2] [,3] [,4]
## cd4_IFNg.IL2_COV2.CON 1123 0 0 139
## cd4_IFNg.IL2_BA.4.5 1127 4 1 130
## cd8_IFNg.IL2_COV2.CON 1123 0 0 139
## cd8_IFNg.IL2_BA.4.5 1127 4 2 129
## cd4_IFNg.IL2.154_COV2.CON 1123 0 0 139
## cd4_IFNg.IL2.154_BA.4.5 1127 4 1 130
## cd4_IL4.IL5.IL13.154_COV2.CON 1123 0 0 139
## cd4_IL4.IL5.IL13.154_BA.4.5 1127 4 1 130
## cd4_IL21_COV2.CON 1123 0 0 139
## cd4_IL21_BA.4.5 1127 4 1 130
## cd4_IFNg_COV2.CON 1123 0 0 139
## cd4_IFNg_BA.4.5 1127 4 1 130
## cd4_IL2_COV2.CON 1123 0 0 139
## cd4_IL2_BA.4.5 1127 4 1 130
## cd4_TNFa_COV2.CON 1123 0 0 139
## cd4_TNFa_BA.4.5 1127 4 1 130
## cd8_IFNg_COV2.CON 1123 0 0 139
## cd8_IFNg_BA.4.5 1127 4 2 129
## cd8_IL2_COV2.CON 1123 0 0 139
## cd8_IL2_BA.4.5 1127 4 2 129
## cd8_TNFa_COV2.CON 1123 0 0 139
## cd8_TNFa_BA.4.5 1127 4 2 129
## cd4_154_COV2.CON 1123 0 0 139
## cd4_154_BA.4.5 1127 4 1 130
## cd4_IL17a_COV2.CON 1123 0 0 139
## cd4_IL17a_BA.4.5 1127 4 1 130
## cd4_IL4.154_COV2.CON 1123 0 0 139
## cd4_IL4.154_BA.4.5 1127 4 1 130
## cd4_IL5.IL13.154_COV2.CON 1123 0 0 139
## cd4_IL5.IL13.154_BA.4.5 1127 4 1 130
## cd4_CXCR5.154_COV2.CON 1123 0 0 139
## cd4_CXCR5.154_BA.4.5 1127 4 1 130
## cd4_CXCR5.IL21_COV2.CON 1123 0 0 139
## cd4_CXCR5.IL21_BA.4.5 1127 4 1 130
## cd8_IFNg.IL2.TNFa_COV2.CON 1123 0 0 139
## cd8_IFNg.IL2.TNFa_BA.4.5 1127 4 2 129i=2; mytable(!is.na(dat_mapped[[glue("B{S1[i]}")]]), !is.na(dat_mapped[[glue("B{S2[i]}")]]))##
## FALSE TRUE
## FALSE 691 1
## TRUE 19 551i=1; mytable(!is.na(dat_mapped[[glue("B{S1[i]}")]]), !is.na(dat_mapped[[glue("B{S2[i]}")]]), dat_mapped$COVIDIndD22toD181)## , , = 0
##
##
## FALSE TRUE
## FALSE 576 0
## TRUE 0 404
##
## , , = 1
##
##
## FALSE TRUE
## FALSE 76 0
## TRUE 0 160
##
## , , = NA
##
##
## FALSE TRUE
## FALSE 34 0
## TRUE 0 12i=1; mytable(!is.na(dat_mapped[[glue("B{S2[i]}")]]), !is.na(dat_mapped[["Day181cd4_IFNg.IL2_Wuhan.N"]]),
dat_mapped$COVIDIndD22toD181)## , , = 0
##
##
## FALSE TRUE
## FALSE 573 3
## TRUE 298 106
##
## , , = 1
##
##
## FALSE TRUE
## FALSE 76 0
## TRUE 144 16
##
## , , = NA
##
##
## FALSE TRUE
## FALSE 34 0
## TRUE 12 0mytable(rowSums(!is.na(dat_mapped[glue("B{tcellvv}")])))##
## 0 36 54 72 85 86 88 90
## 686 5 18 38 3 3 11 498t(dat_mapped[which(rowSums(!is.na(dat_mapped[glue("B{tcellvv}")]))==36),201:300])## 188 306 541
## Bcd4_CXCR5.IL21_BA.4.5.S2 NA NA NA
## Bcd4_CXCR5.IL21_COV2.CON.S1 0.014437157 0.004467056 0.001000000
## Bcd4_CXCR5.IL21_COV2.CON.S2 0.001000000 0.001000000 0.001000000
## Bcd4_CXCR5.IL21_Wuhan.N NA NA NA
## Bcd4_IFNg_BA.4.5.S1 NA NA NA
## Bcd4_IFNg_BA.4.5.S2 NA NA NA
## Bcd4_IFNg_COV2.CON.S1 0.231073038 0.049872446 0.089807106
## Bcd4_IFNg_COV2.CON.S2 0.131226468 0.043148201 0.103975807
## Bcd4_IFNg_Wuhan.N NA NA NA
## Bcd4_IFNg.IL2_BA.4.5.S1 NA NA NA
## Bcd4_IFNg.IL2_BA.4.5.S2 NA NA NA
## Bcd4_IFNg.IL2_COV2.CON.S1 0.368800537 0.071189236 0.082697693
## Bcd4_IFNg.IL2_COV2.CON.S2 0.238105378 0.065693293 0.099041092
## Bcd4_IFNg.IL2_Wuhan.N NA NA NA
## Bcd4_IFNg.IL2.154_BA.4.5.S1 NA NA NA
## Bcd4_IFNg.IL2.154_BA.4.5.S2 NA NA NA
## Bcd4_IFNg.IL2.154_COV2.CON.S1 0.418913129 0.074412217 0.082763326
## Bcd4_IFNg.IL2.154_COV2.CON.S2 0.264305574 0.076316213 0.100689069
## Bcd4_IFNg.IL2.154_Wuhan.N NA NA NA
## Bcd4_IL17a_BA.4.5.S1 NA NA NA
## Bcd4_IL17a_BA.4.5.S2 NA NA NA
## Bcd4_IL17a_COV2.CON.S1 0.004124902 0.001000000 0.004358710
## Bcd4_IL17a_COV2.CON.S2 0.003980733 0.003144674 0.010688086
## Bcd4_IL17a_Wuhan.N NA NA NA
## Bcd4_IL2_BA.4.5.S1 NA NA NA
## Bcd4_IL2_BA.4.5.S2 NA NA NA
## Bcd4_IL2_COV2.CON.S1 0.269267644 0.063424004 0.038801777
## Bcd4_IL2_COV2.CON.S2 0.185892686 0.061919684 0.050058636
## Bcd4_IL2_Wuhan.N NA NA NA
## Bcd4_IL21_BA.4.5.S1 NA NA NA
## Bcd4_IL21_BA.4.5.S2 NA NA NA
## Bcd4_IL21_COV2.CON.S1 0.076389212 0.007690037 0.050302716
## Bcd4_IL21_COV2.CON.S2 0.009814104 0.003759814 0.054059757
## Bcd4_IL21_Wuhan.N NA NA NA
## Bcd4_IL4.154_BA.4.5.S1 NA NA NA
## Bcd4_IL4.154_BA.4.5.S2 NA NA NA
## Bcd4_IL4.154_COV2.CON.S1 0.001000000 0.001000000 0.001000000
## Bcd4_IL4.154_COV2.CON.S2 0.001000000 0.001000000 0.001000000
## Bcd4_IL4.154_Wuhan.N NA NA NA
## Bcd4_IL4.IL5.IL13.154_BA.4.5.S1 NA NA NA
## Bcd4_IL4.IL5.IL13.154_BA.4.5.S2 NA NA NA
## Bcd4_IL4.IL5.IL13.154_COV2.CON.S1 0.001000000 0.001903710 0.001000000
## Bcd4_IL4.IL5.IL13.154_COV2.CON.S2 0.001000000 0.001873010 0.001000000
## Bcd4_IL4.IL5.IL13.154_Wuhan.N NA NA NA
## Bcd4_IL5.IL13.154_BA.4.5.S1 NA NA NA
## Bcd4_IL5.IL13.154_BA.4.5.S2 NA NA NA
## Bcd4_IL5.IL13.154_COV2.CON.S1 0.001000000 0.001903710 0.001000000
## Bcd4_IL5.IL13.154_COV2.CON.S2 0.001990367 0.001873010 0.001000000
## Bcd4_IL5.IL13.154_Wuhan.N NA NA NA
## Bcd4_TNFa_BA.4.5.S1 NA NA NA
## Bcd4_TNFa_BA.4.5.S2 NA NA NA
## Bcd4_TNFa_COV2.CON.S1 0.355526818 0.001000000 0.001000000
## Bcd4_TNFa_COV2.CON.S2 0.253389639 0.001000000 0.001000000
## Bcd4_TNFa_Wuhan.N NA NA NA
## Bcd8_IFNg_BA.4.5.S1 NA NA NA
## Bcd8_IFNg_BA.4.5.S2 NA NA NA
## Bcd8_IFNg_COV2.CON.S1 0.004075968 0.001000000 0.017488870
## Bcd8_IFNg_COV2.CON.S2 0.006330431 0.003894445 0.001000000
## Bcd8_IFNg_Wuhan.N NA NA NA
## Bcd8_IFNg.IL2_BA.4.5.S1 NA NA NA
## Bcd8_IFNg.IL2_BA.4.5.S2 NA NA NA
## Bcd8_IFNg.IL2_COV2.CON.S1 0.004075968 0.001000000 0.033458209
## Bcd8_IFNg.IL2_COV2.CON.S2 0.008633358 0.003894445 0.012971951
## Bcd8_IFNg.IL2_Wuhan.N NA NA NA
## Bcd8_IFNg.IL2.TNFa_BA.4.5.S1 NA NA NA
## Bcd8_IFNg.IL2.TNFa_BA.4.5.S2 NA NA NA
## Bcd8_IFNg.IL2.TNFa_COV2.CON.S1 0.010023720 0.001000000 0.005209820
## Bcd8_IFNg.IL2.TNFa_COV2.CON.S2 0.014532644 0.005627565 0.001000000
## Bcd8_IFNg.IL2.TNFa_Wuhan.N NA NA NA
## Bcd8_IL2_BA.4.5.S1 NA NA NA
## Bcd8_IL2_BA.4.5.S2 NA NA NA
## Bcd8_IL2_COV2.CON.S1 0.001000000 0.001000000 0.008201745
## Bcd8_IL2_COV2.CON.S2 0.002302927 0.001000000 0.029646354
## Bcd8_IL2_Wuhan.N NA NA NA
## Bcd8_TNFa_BA.4.5.S1 NA NA NA
## Bcd8_TNFa_BA.4.5.S2 NA NA NA
## Bcd8_TNFa_COV2.CON.S1 0.005947751 0.001000000 0.012760338
## Bcd8_TNFa_COV2.CON.S2 0.008202214 0.001000000 0.001000000
## Bcd8_TNFa_Wuhan.N NA NA NA
## Day15cd4_154_BA.4.5.S1 0.670446287 0.138362130 0.093446034
## Day15cd4_154_BA.4.5.S2 0.441614813 NA 0.074328671
## Day15cd4_154_COV2.CON.S1 0.732183666 0.180064972 0.128435391
## Day15cd4_154_COV2.CON.S2 0.424804333 0.193484068 0.089379914
## Day15cd4_154_Wuhan.N 0.039243080 NA 0.073720925
## Day15cd4_CXCR5.154_BA.4.5.S1 0.038345029 0.018148994 0.004033772
## Day15cd4_CXCR5.154_BA.4.5.S2 0.023400806 NA 0.006143236
## Day15cd4_CXCR5.154_COV2.CON.S1 0.032358148 0.033012250 0.006973130
## Day15cd4_CXCR5.154_COV2.CON.S2 0.008763167 0.024092413 0.008965998
## Day15cd4_CXCR5.154_Wuhan.N 0.006753563 NA 0.011745836
## Day15cd4_CXCR5.IL21_BA.4.5.S1 0.021089766 0.002865631 0.001000000
## Day15cd4_CXCR5.IL21_BA.4.5.S2 0.011700403 NA 0.002546184
## Day15cd4_CXCR5.IL21_COV2.CON.S1 0.013323943 0.007074054 0.001086433
## Day15cd4_CXCR5.IL21_COV2.CON.S2 0.003505267 0.010144174 0.001721183
## Day15cd4_CXCR5.IL21_Wuhan.N 0.001688391 NA 0.001743479
## Day15cd4_IFNg_BA.4.5.S1 0.568500577 0.112581012 0.097479806
## Day15cd4_IFNg_BA.4.5.S2 0.346254485 NA 0.071617099
## Day15cd4_IFNg_COV2.CON.S1 0.651922454 0.146838480 0.126505564
## Day15cd4_IFNg_COV2.CON.S2 0.333500633 0.171624443 0.097445311
## Day15cd4_IFNg_Wuhan.N 0.042517337 NA 0.065530917
## Day15cd4_IFNg.IL2_BA.4.5.S1 0.710377183 0.151553499 0.096430692
## 542 546
## Bcd4_CXCR5.IL21_BA.4.5.S2 NA NA
## Bcd4_CXCR5.IL21_COV2.CON.S1 0.003822271 0.001000000
## Bcd4_CXCR5.IL21_COV2.CON.S2 0.001000000 0.001000000
## Bcd4_CXCR5.IL21_Wuhan.N NA NA
## Bcd4_IFNg_BA.4.5.S1 NA NA
## Bcd4_IFNg_BA.4.5.S2 NA NA
## Bcd4_IFNg_COV2.CON.S1 0.069755310 0.030218373
## Bcd4_IFNg_COV2.CON.S2 0.125680722 0.039321171
## Bcd4_IFNg_Wuhan.N NA NA
## Bcd4_IFNg.IL2_BA.4.5.S1 NA NA
## Bcd4_IFNg.IL2_BA.4.5.S2 NA NA
## Bcd4_IFNg.IL2_COV2.CON.S1 0.116576996 0.023255226
## Bcd4_IFNg.IL2_COV2.CON.S2 0.224547861 0.051406264
## Bcd4_IFNg.IL2_Wuhan.N NA NA
## Bcd4_IFNg.IL2.154_BA.4.5.S1 NA NA
## Bcd4_IFNg.IL2.154_BA.4.5.S2 NA NA
## Bcd4_IFNg.IL2.154_COV2.CON.S1 0.129272234 0.025418511
## Bcd4_IFNg.IL2.154_COV2.CON.S2 0.233485722 0.051406264
## Bcd4_IFNg.IL2.154_Wuhan.N NA NA
## Bcd4_IL17a_BA.4.5.S1 NA NA
## Bcd4_IL17a_BA.4.5.S2 NA NA
## Bcd4_IL17a_COV2.CON.S1 0.001000000 0.002095672
## Bcd4_IL17a_COV2.CON.S2 0.002012079 0.001000000
## Bcd4_IL17a_Wuhan.N NA NA
## Bcd4_IL2_BA.4.5.S1 NA NA
## Bcd4_IL2_BA.4.5.S2 NA NA
## Bcd4_IL2_COV2.CON.S1 0.108898207 0.012506416
## Bcd4_IL2_COV2.CON.S2 0.188351643 0.043743813
## Bcd4_IL2_Wuhan.N NA NA
## Bcd4_IL21_BA.4.5.S1 NA NA
## Bcd4_IL21_BA.4.5.S2 NA NA
## Bcd4_IL21_COV2.CON.S1 0.003073321 0.001000000
## Bcd4_IL21_COV2.CON.S2 0.003134613 0.001000000
## Bcd4_IL21_Wuhan.N NA NA
## Bcd4_IL4.154_BA.4.5.S1 NA NA
## Bcd4_IL4.154_BA.4.5.S2 NA NA
## Bcd4_IL4.154_COV2.CON.S1 0.001000000 0.001000000
## Bcd4_IL4.154_COV2.CON.S2 0.001000000 0.001978670
## Bcd4_IL4.154_Wuhan.N NA NA
## Bcd4_IL4.IL5.IL13.154_BA.4.5.S1 NA NA
## Bcd4_IL4.IL5.IL13.154_BA.4.5.S2 NA NA
## Bcd4_IL4.IL5.IL13.154_COV2.CON.S1 0.001000000 0.001000000
## Bcd4_IL4.IL5.IL13.154_COV2.CON.S2 0.001000000 0.001000000
## Bcd4_IL4.IL5.IL13.154_Wuhan.N NA NA
## Bcd4_IL5.IL13.154_BA.4.5.S1 NA NA
## Bcd4_IL5.IL13.154_BA.4.5.S2 NA NA
## Bcd4_IL5.IL13.154_COV2.CON.S1 0.001000000 0.001000000
## Bcd4_IL5.IL13.154_COV2.CON.S2 0.001000000 0.001000000
## Bcd4_IL5.IL13.154_Wuhan.N NA NA
## Bcd4_TNFa_BA.4.5.S1 NA NA
## Bcd4_TNFa_BA.4.5.S2 NA NA
## Bcd4_TNFa_COV2.CON.S1 0.087647264 0.001000000
## Bcd4_TNFa_COV2.CON.S2 0.169840571 0.001000000
## Bcd4_TNFa_Wuhan.N NA NA
## Bcd8_IFNg_BA.4.5.S1 NA NA
## Bcd8_IFNg_BA.4.5.S2 NA NA
## Bcd8_IFNg_COV2.CON.S1 0.025910547 0.019815029
## Bcd8_IFNg_COV2.CON.S2 0.145304185 0.032749356
## Bcd8_IFNg_Wuhan.N NA NA
## Bcd8_IFNg.IL2_BA.4.5.S1 NA NA
## Bcd8_IFNg.IL2_BA.4.5.S2 NA NA
## Bcd8_IFNg.IL2_COV2.CON.S1 0.022343196 0.016886600
## Bcd8_IFNg.IL2_COV2.CON.S2 0.159070861 0.029820927
## Bcd8_IFNg.IL2_Wuhan.N NA NA
## Bcd8_IFNg.IL2.TNFa_BA.4.5.S1 NA NA
## Bcd8_IFNg.IL2.TNFa_BA.4.5.S2 NA NA
## Bcd8_IFNg.IL2.TNFa_COV2.CON.S1 0.022718830 0.022563465
## Bcd8_IFNg.IL2.TNFa_COV2.CON.S2 0.144801454 0.029182500
## Bcd8_IFNg.IL2.TNFa_Wuhan.N NA NA
## Bcd8_IL2_BA.4.5.S1 NA NA
## Bcd8_IL2_BA.4.5.S2 NA NA
## Bcd8_IL2_COV2.CON.S1 0.003849077 0.001000000
## Bcd8_IL2_COV2.CON.S2 0.096869457 0.001000000
## Bcd8_IL2_Wuhan.N NA NA
## Bcd8_TNFa_BA.4.5.S1 NA NA
## Bcd8_TNFa_BA.4.5.S2 NA NA
## Bcd8_TNFa_COV2.CON.S1 0.022718830 0.028672314
## Bcd8_TNFa_COV2.CON.S2 0.127467427 0.017519679
## Bcd8_TNFa_Wuhan.N NA NA
## Day15cd4_154_BA.4.5.S1 0.099916638 0.101356502
## Day15cd4_154_BA.4.5.S2 0.246679127 0.089917283
## Day15cd4_154_COV2.CON.S1 0.143497231 0.083196271
## Day15cd4_154_COV2.CON.S2 0.236429577 0.100542189
## Day15cd4_154_Wuhan.N 0.001000000 0.011245048
## Day15cd4_CXCR5.154_BA.4.5.S1 0.012290861 0.001911096
## Day15cd4_CXCR5.154_BA.4.5.S2 0.043487000 0.003336726
## Day15cd4_CXCR5.154_COV2.CON.S1 0.017738435 0.003865407
## Day15cd4_CXCR5.154_COV2.CON.S2 0.036133347 0.005295769
## Day15cd4_CXCR5.154_Wuhan.N 0.001000000 0.001000000
## Day15cd4_CXCR5.IL21_BA.4.5.S1 0.001000000 0.005801711
## Day15cd4_CXCR5.IL21_BA.4.5.S2 0.003143008 0.003162416
## Day15cd4_CXCR5.IL21_COV2.CON.S1 0.005689719 0.001000000
## Day15cd4_CXCR5.IL21_COV2.CON.S2 0.009385890 0.001687840
## Day15cd4_CXCR5.IL21_Wuhan.N 0.002376547 0.001000000
## Day15cd4_IFNg_BA.4.5.S1 0.070479141 0.097465887
## Day15cd4_IFNg_BA.4.5.S2 0.175513422 0.105106859
## Day15cd4_IFNg_COV2.CON.S1 0.106430611 0.085038944
## Day15cd4_IFNg_COV2.CON.S2 0.194794945 0.125333192
## Day15cd4_IFNg_Wuhan.N 0.001000000 0.011451755
## Day15cd4_IFNg.IL2_BA.4.5.S1 0.104366912 0.110911981# cross-tabulate missingness between baseline and D181
tab=mysapply (1:length(S1), function(i) {
c(mytable(!is.na(dat_mapped[[glue("B{S1[i]}")]]), !is.na(dat_mapped[[glue("Day181{S1[i]}")]])))
})
rownames(tab)=sub(".S1", "", S1); tab## [,1] [,2] [,3] [,4]
## cd4_IFNg.IL2_COV2.CON 683 440 3 136
## cd4_IFNg.IL2_BA.4.5 688 440 4 130
## cd8_IFNg.IL2_COV2.CON 683 440 3 136
## cd8_IFNg.IL2_BA.4.5 690 439 4 129
## cd4_IFNg.IL2.154_COV2.CON 683 440 3 136
## cd4_IFNg.IL2.154_BA.4.5 688 440 4 130
## cd4_IL4.IL5.IL13.154_COV2.CON 683 440 3 136
## cd4_IL4.IL5.IL13.154_BA.4.5 688 440 4 130
## cd4_IL21_COV2.CON 683 440 3 136
## cd4_IL21_BA.4.5 688 440 4 130
## cd4_IFNg_COV2.CON 683 440 3 136
## cd4_IFNg_BA.4.5 688 440 4 130
## cd4_IL2_COV2.CON 683 440 3 136
## cd4_IL2_BA.4.5 688 440 4 130
## cd4_TNFa_COV2.CON 683 440 3 136
## cd4_TNFa_BA.4.5 688 440 4 130
## cd8_IFNg_COV2.CON 683 440 3 136
## cd8_IFNg_BA.4.5 690 439 4 129
## cd8_IL2_COV2.CON 683 440 3 136
## cd8_IL2_BA.4.5 690 439 4 129
## cd8_TNFa_COV2.CON 683 440 3 136
## cd8_TNFa_BA.4.5 690 439 4 129
## cd4_154_COV2.CON 683 440 3 136
## cd4_154_BA.4.5 688 440 4 130
## cd4_IL17a_COV2.CON 683 440 3 136
## cd4_IL17a_BA.4.5 688 440 4 130
## cd4_IL4.154_COV2.CON 683 440 3 136
## cd4_IL4.154_BA.4.5 688 440 4 130
## cd4_IL5.IL13.154_COV2.CON 683 440 3 136
## cd4_IL5.IL13.154_BA.4.5 688 440 4 130
## cd4_CXCR5.154_COV2.CON 683 440 4 135
## cd4_CXCR5.154_BA.4.5 696 432 4 130
## cd4_CXCR5.IL21_COV2.CON 683 440 4 135
## cd4_CXCR5.IL21_BA.4.5 696 432 4 130
## cd8_IFNg.IL2.TNFa_COV2.CON 683 440 3 136
## cd8_IFNg.IL2.TNFa_BA.4.5 690 439 4 129mypairs(dat_mapped[,c(glue("B{c(rbind(S1[1:2]))}"), glue("Day181{c(rbind(S1[1:2]))}"))], gap=0)
explore the second order effect
coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ FOIstandardized + standardized_risk_score + scale(Bpseudoneutid50_MDW, scale=F) + I(scale(Day15pseudoneutid50_MDW, scale=F)^2) + scale(Day15pseudoneutid50_MDW, scale=F), subset(dat, naive==1))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## FOIstandardized + standardized_risk_score + scale(Bpseudoneutid50_MDW,
## scale = F) + I(scale(Day15pseudoneutid50_MDW, scale = F)^2) +
## scale(Day15pseudoneutid50_MDW, scale = F), data = subset(dat,
## naive == 1))
##
## coef exp(coef) se(coef)
## FOIstandardized -0.07363 0.92902 0.09311
## standardized_risk_score 0.27288 1.31374 0.09695
## scale(Bpseudoneutid50_MDW, scale = F) -0.36342 0.69530 0.17576
## I(scale(Day15pseudoneutid50_MDW, scale = F)^2) -0.88931 0.41094 0.30418
## scale(Day15pseudoneutid50_MDW, scale = F) -0.12800 0.87985 0.24588
## z p
## FOIstandardized -0.791 0.42908
## standardized_risk_score 2.815 0.00488
## scale(Bpseudoneutid50_MDW, scale = F) -2.068 0.03867
## I(scale(Day15pseudoneutid50_MDW, scale = F)^2) -2.924 0.00346
## scale(Day15pseudoneutid50_MDW, scale = F) -0.521 0.60266
##
## Likelihood ratio test=26.07 on 5 df, p=8.656e-05
## n= 629, number of events= 181coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ scale(Bpseudoneutid50_MDW, scale=F) + I(scale(Day15pseudoneutid50_MDW, scale=F)^2) + scale(Day15pseudoneutid50_MDW, scale=F), subset(dat, naive==1))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## scale(Bpseudoneutid50_MDW, scale = F) + I(scale(Day15pseudoneutid50_MDW,
## scale = F)^2) + scale(Day15pseudoneutid50_MDW, scale = F),
## data = subset(dat, naive == 1))
##
## coef exp(coef) se(coef)
## scale(Bpseudoneutid50_MDW, scale = F) -0.2898 0.7484 0.1741
## I(scale(Day15pseudoneutid50_MDW, scale = F)^2) -0.8976 0.4075 0.3036
## scale(Day15pseudoneutid50_MDW, scale = F) -0.1913 0.8259 0.2443
## z p
## scale(Bpseudoneutid50_MDW, scale = F) -1.664 0.09604
## I(scale(Day15pseudoneutid50_MDW, scale = F)^2) -2.957 0.00311
## scale(Day15pseudoneutid50_MDW, scale = F) -0.783 0.43363
##
## Likelihood ratio test=17.69 on 3 df, p=0.00051
## n= 629, number of events= 181plot(Day15pseudoneutid50_MDW~Bpseudoneutid50_MDW, subset(dat, naive==1), col=subset(dat, naive==1)$COVIDIndD22toD181+1)
plot(Day15pseudoneutid50_MDW~Bpseudoneutid50_MDW, subset(dat, naive==0), col=subset(dat, naive==0)$COVIDIndD22toD181+1)
scatterplot of D15 and B
- Make all plots in IU.
- COVAIL is dose 4. COVE booster is dose 3.
plot_anc_BA1=function(tmp, main) {
par(mfrow=c(1,2), oma=c(0,2.5,3,0.5), mar=c(3,0,2,0), mgp = c(2, .5, 0))
lim=c(0,4.5)
plot(Day15pseudoneutid50_D614G~Bpseudoneutid50_D614G, tmp, col=ifelse(tmp$naive==1,1,2), main="Ancestral ID50", xlab="D1 (IU/ml)", xlim=lim, ylim=lim, asp=1); abline(0,1)
plot(Day15pseudoneutid50_BA.1~Bpseudoneutid50_BA.1, tmp, col=ifelse(tmp$naive==1,1,2), main="BA1 ID50", xlab="D1 (IU/ml)", xlim=lim, ylim=lim, asp=1); abline(0,1)
title(ylab="D15 (IU/ml)", outer=T, line=1.5)
title(main=main, outer=T, line=0)
}BA1 DI50 in one row and Ancestral ID50 in second row, three columns for three vaccines
tmp=convert2IU(subset(dat_proc, ph1.D15==1 & treatment_assigned %in% c(
"1 Dose Prototype (Moderna)" # 50 ug
, "1 Dose Omicron (Moderna)" # BA1
, "Beta (Sanofi)", "Beta + Prototype (Sanofi)", "Prototype (Sanofi)"
)
))
tmp$col=ifelse(tmp$naive==1,1,2); lim=c(0,4.5)
par(mfrow=c(2,3), oma=c(2.5,3.5,3,0.5), mar=c(0,0,2,0), mgp = c(2, .5, 0))
kp=tmp$treatment_assigned=="1 Dose Prototype (Moderna)"; plot(Day15pseudoneutid50_BA.1~Bpseudoneutid50_BA.1, tmp[kp,], col=tmp$col[kp], main="", xlab="D1 (IU/ml)", xlim=lim, ylim=lim, asp=1); abline(0,1); title(main="Moderna Prototype", line=1)
kp=tmp$treatment_assigned=="1 Dose Omicron (Moderna)"; plot(Day15pseudoneutid50_BA.1~Bpseudoneutid50_BA.1, tmp[kp,], col=tmp$col[kp], main="", xlab="D1 (IU/ml)", xlim=lim, ylim=lim, asp=1); abline(0,1); title(main="Moderna BA.1", line=1)
kp=tmp$treatment_assigned %in% c("Beta (Sanofi)", "Beta + Prototype (Sanofi)", "Prototype (Sanofi)"); plot(Day15pseudoneutid50_BA.1~Bpseudoneutid50_BA.1, tmp[kp,], col=tmp$col[kp], main="", xlab="D1 (IU/ml)", xlim=lim, ylim=lim, asp=1); abline(0,1); title(main="Sanofi", line=1)
par(mar=c(0,0,0,0))
kp=tmp$treatment_assigned=="1 Dose Prototype (Moderna)"; plot(Day15pseudoneutid50_D614G~Bpseudoneutid50_D614G, tmp[kp,], col=tmp$col[kp], main="", xlab="D1 (IU/ml)", xlim=lim, ylim=lim, asp=1); abline(0,1)
kp=tmp$treatment_assigned=="1 Dose Omicron (Moderna)"; plot(Day15pseudoneutid50_D614G~Bpseudoneutid50_D614G, tmp[kp,], col=tmp$col[kp], main="", xlab="D1 (IU/ml)", xlim=lim, ylim=lim, asp=1); abline(0,1)
kp=tmp$treatment_assigned %in% c("Beta (Sanofi)", "Beta + Prototype (Sanofi)", "Prototype (Sanofi)"); plot(Day15pseudoneutid50_D614G~Bpseudoneutid50_D614G, tmp[kp,], col=tmp$col[kp], main="", xlab="D1 (IU/ml)", xlim=lim, ylim=lim, asp=1); abline(0,1)
title(ylab="Ancestral ID50 BA.1 ID50", outer=T, line=2.5, font.lab=2)
title(ylab="D15 (IU/ml)", outer=T, line=1.5)
title(xlab="D1 (IU/ml)", outer=T, line=1.5) 
COVAIL Sanofi
tmp=convert2IU(dat.sanofi)
plot_anc_BA1(tmp, main="COVAIL Sanofi")
Three Sanofi arms plotted separately
tmp=convert2IU(subset(dat_proc, ph1.D15==1 & treatment_assigned %in% c(
"Beta (Sanofi)", "Beta + Prototype (Sanofi)", "Prototype (Sanofi)"
)
))
tmp$col=ifelse(tmp$naive==1,1,2); lim=c(0,4.5)
par(mfrow=c(2,3), oma=c(2.5,3.5,3,0.5), mar=c(0,0,2,0), mgp = c(2, .5, 0))
kp=tmp$treatment_assigned %in% c("Prototype (Sanofi)"); plot(Day15pseudoneutid50_BA.1~Bpseudoneutid50_BA.1, tmp[kp,], col=tmp$col[kp], main="", xlab="D1 (IU/ml)", xlim=lim, ylim=lim, asp=1); abline(0,1); title(main="Prototype", line=1)
kp=tmp$treatment_assigned=="Beta (Sanofi)"; plot(Day15pseudoneutid50_BA.1~Bpseudoneutid50_BA.1, tmp[kp,], col=tmp$col[kp], main="", xlab="D1 (IU/ml)", xlim=lim, ylim=lim, asp=1); abline(0,1); title(main="Beta", line=1)
kp=tmp$treatment_assigned=="Beta + Prototype (Sanofi)"; plot(Day15pseudoneutid50_BA.1~Bpseudoneutid50_BA.1, tmp[kp,], col=tmp$col[kp], main="", xlab="D1 (IU/ml)", xlim=lim, ylim=lim, asp=1); abline(0,1); title(main="Prototype + Beta", line=1)
par(mar=c(0,0,0,0))
kp=tmp$treatment_assigned %in% c("Prototype (Sanofi)"); plot(Day15pseudoneutid50_D614G~Bpseudoneutid50_D614G, tmp[kp,], col=tmp$col[kp], main="", xlab="D1 (IU/ml)", xlim=lim, ylim=lim, asp=1); abline(0,1)
kp=tmp$treatment_assigned=="Beta (Sanofi)"; plot(Day15pseudoneutid50_D614G~Bpseudoneutid50_D614G, tmp[kp,], col=tmp$col[kp], main="", xlab="D1 (IU/ml)", xlim=lim, ylim=lim, asp=1); abline(0,1)
kp=tmp$treatment_assigned=="Beta + Prototype (Sanofi)"; plot(Day15pseudoneutid50_D614G~Bpseudoneutid50_D614G, tmp[kp,], col=tmp$col[kp], main="", xlab="D1 (IU/ml)", xlim=lim, ylim=lim, asp=1); abline(0,1)
title(ylab="Ancestral ID50 BA.1 ID50", outer=T, line=2.5, font.lab=2)
title(ylab="D15 (IU/ml)", outer=T, line=1.5)
title(xlab="D1 (IU/ml)", outer=T, line=1.5)
title(main="Sanoifi", outer=T, line=1.5) 
COVAIL moderna prototype
tmp=convert2IU(subset(dat_proc, ph1.D15==1 & treatment_assigned %in% c(
"1 Dose Prototype (Moderna)" # 50 ug
)
))
plot_anc_BA1(tmp, main="COVAIL Moderna Prototype (50ug)") # COVAIL mRNA-1273 Prototype Dose 4 (50ug)
COVAIL moderna Omicron BA1
tmp=convert2IU(subset(dat_proc, ph1.D15==1 & treatment_assigned %in% c(
"1 Dose Omicron (Moderna)" # BA1
)
))
plot_anc_BA1(tmp, main="COVAIL Moderna BA.1 (50ug)") # COVAIL mRNA-1273 BA.1 Dose 4 (50ug)
COVAIL moderna prototype and BA1 together
tmp=convert2IU(subset(dat_proc, ph1.D15==1 & treatment_assigned %in% c(
"1 Dose Prototype (Moderna)" # 50 ug
,"1 Dose Omicron (Moderna)" # BA1
)
))
tmp$trt=ifelse(tmp$treatment_assigned=="1 Dose Prototype (Moderna)",1,0)
main="COVAIL Moderna (50ug)"
par(mfrow=c(1,2), oma=c(0,2.5,3,0.5), mar=c(3,0,2,0), mgp = c(2, .5, 0))
lim=c(0,4.5)
plot(Day15pseudoneutid50_D614G~Bpseudoneutid50_D614G, tmp, pch=ifelse(tmp$naive==1,1,2), col=ifelse(tmp$trt==1,1,2), main="Ancestral ID50", xlab="D1 (IU/ml)", xlim=lim, ylim=lim, asp=1); abline(0,1)
plot(Day15pseudoneutid50_BA.1~Bpseudoneutid50_BA.1, tmp, pch=ifelse(tmp$naive==1,1,2), col=ifelse(tmp$trt==1,1,2), main="BA1 ID50", xlab="D1 (IU/ml)", xlim=lim, ylim=lim, asp=1); abline(0,1)
# mylegend(legend=c())
title(ylab="D15 (IU/ml)", outer=T, line=1.5)
title(main=main, outer=T, line=0)
COVAIL mRNA
tmp=convert2IU(dat.ocp)
plot_anc_BA1(tmp, main="COVAIL Omi/Prot 1 dose mRNA")
Cox model using a generated marker based on baseline
tmp=convert2IU(subset(dat_proc, ph1.D15==1 & treatment_assigned %in% c(
"1 Dose Omicron (Moderna)" # BA1
)))
tmp1 = subset(tmp, Bpseudoneutid50_D614G>0.5 & Day15pseudoneutid50_D614G<4)
fit=lm(Day15pseudoneutid50_D614G~Bpseudoneutid50_D614G+I(Bpseudoneutid50_D614G^2)+I(Bpseudoneutid50_D614G^3), tmp1)
tmp$pred=predict(fit,newdata=tmp)
tmp$resid=tmp$Day15pseudoneutid50_D614G-tmp$pred
tmp1$pred=predict(fit,newdata=tmp1)
plot(Day15pseudoneutid50_D614G~Bpseudoneutid50_D614G, tmp, xlim=c(0,5), ylim=c(0,5), asp=1); mylines(tmp$Bpseudoneutid50_D614G, tmp$pred, col=2)
par(mfrow=c(1,2))
lim=NULL
corplot(Day15pseudoneutid50_D614G~resid, tmp, xlim=NULL, ylim=c(0,5), asp=1)
corplot(Bpseudoneutid50_D614G~resid, tmp, xlim=NULL, ylim=c(0,5), asp=1)
coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ naive + Day15pseudoneutid50_D614G, tmp)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## naive + Day15pseudoneutid50_D614G, data = tmp)
##
## coef exp(coef) se(coef) z p
## naive 1.9245 6.8514 1.0371 1.856 0.0635
## Day15pseudoneutid50_D614G -1.0112 0.3638 0.4784 -2.114 0.0345
##
## Likelihood ratio test=14.4 on 2 df, p=0.0007468
## n= 96, number of events= 25coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ naive + Bpseudoneutid50_D614G, tmp)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## naive + Bpseudoneutid50_D614G, data = tmp)
##
## coef exp(coef) se(coef) z p
## naive 2.0548 7.8054 1.0389 1.978 0.0479
## Bpseudoneutid50_D614G -0.3143 0.7303 0.3050 -1.031 0.3028
##
## Likelihood ratio test=10.94 on 2 df, p=0.004209
## n= 96, number of events= 25coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ naive + pred, tmp)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## naive + pred, data = tmp)
##
## coef exp(coef) se(coef) z p
## naive 2.2008 9.0320 1.0299 2.137 0.0326
## pred -0.1359 0.8730 0.4590 -0.296 0.7673
##
## Likelihood ratio test=10.01 on 2 df, p=0.006712
## n= 96, number of events= 25coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ naive + resid, tmp)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## naive + resid, data = tmp)
##
## coef exp(coef) se(coef) z p
## naive 2.1879 8.9161 1.0225 2.140 0.0324
## resid -1.4238 0.2408 0.7234 -1.968 0.0491
##
## Likelihood ratio test=14.62 on 2 df, p=0.0006689
## n= 96, number of events= 25coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ pred + resid, tmp)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## pred + resid, data = tmp)
##
## coef exp(coef) se(coef) z p
## pred -1.1754 0.3087 0.5867 -2.003 0.0451
## resid -1.6826 0.1859 0.6816 -2.468 0.0136
##
## Likelihood ratio test=8.85 on 2 df, p=0.01196
## n= 96, number of events= 25coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ pred * resid, tmp)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## pred * resid, data = tmp)
##
## coef exp(coef) se(coef) z p
## pred -1.158783 0.313868 0.668844 -1.733 0.0832
## resid -4.788847 0.008322 7.790670 -0.615 0.5388
## pred:resid 1.073863 2.926663 2.564098 0.419 0.6754
##
## Likelihood ratio test=9.24 on 3 df, p=0.02627
## n= 96, number of events= 25coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ Bpseudoneutid50_D614G + resid, tmp)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## Bpseudoneutid50_D614G + resid, data = tmp)
##
## coef exp(coef) se(coef) z p
## Bpseudoneutid50_D614G -0.8558 0.4249 0.3560 -2.404 0.0162
## resid -1.6077 0.2004 0.7080 -2.271 0.0232
##
## Likelihood ratio test=11.28 on 2 df, p=0.003547
## n= 96, number of events= 25coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ Bpseudoneutid50_D614G * resid, tmp)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## Bpseudoneutid50_D614G * resid, data = tmp)
##
## coef exp(coef) se(coef) z p
## Bpseudoneutid50_D614G -0.8440 0.4300 0.3656 -2.309 0.021
## resid -2.2611 0.1042 2.5370 -0.891 0.373
## Bpseudoneutid50_D614G:resid 0.3220 1.3799 1.1381 0.283 0.777
##
## Likelihood ratio test=11.38 on 3 df, p=0.009818
## n= 96, number of events= 25coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ Day15pseudoneutid50_D614G + resid, tmp)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## Day15pseudoneutid50_D614G + resid, data = tmp)
##
## coef exp(coef) se(coef) z p
## Day15pseudoneutid50_D614G -1.1754 0.3087 0.5867 -2.003 0.0451
## resid -0.5072 0.6022 0.8296 -0.611 0.5410
##
## Likelihood ratio test=8.85 on 2 df, p=0.01196
## n= 96, number of events= 25# coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ naive + Day15pseudoneutid50_D614G, tmp1)
# coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ naive + pred, tmp1)
coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ naive + Bpseudoneutid50_D614G * resid, tmp)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## naive + Bpseudoneutid50_D614G * resid, data = tmp)
##
## coef exp(coef) se(coef) z p
## naive 1.8667 6.4668 1.0507 1.777 0.0756
## Bpseudoneutid50_D614G -0.5178 0.5959 0.3842 -1.348 0.1778
## resid -2.0123 0.1337 2.5373 -0.793 0.4277
## Bpseudoneutid50_D614G:resid 0.3010 1.3513 1.1426 0.263 0.7922
##
## Likelihood ratio test=16.55 on 4 df, p=0.00236
## n= 96, number of events= 25coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ naive * resid, tmp)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## naive * resid, data = tmp)
##
## coef exp(coef) se(coef) z p
## naive 2.906515 18.292940 1.584661 1.834 0.0666
## resid -5.713483 0.003301 3.799761 -1.504 0.1327
## naive:resid 4.446396 85.318940 3.867496 1.150 0.2503
##
## Likelihood ratio test=15.99 on 3 df, p=0.001141
## n= 96, number of events= 25coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ naive + pred + resid, tmp)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## naive + pred + resid, data = tmp)
##
## coef exp(coef) se(coef) z p
## naive 2.0212 7.5476 1.0399 1.944 0.0519
## pred -0.6244 0.5356 0.6384 -0.978 0.3281
## resid -1.4546 0.2335 0.6894 -2.110 0.0349
##
## Likelihood ratio test=15.51 on 3 df, p=0.001428
## n= 96, number of events= 25repeat for BA1 marker. not significant
tmp=convert2IU(subset(dat_proc, ph1.D15==1 & treatment_assigned %in% c(
"1 Dose Omicron (Moderna)" # BA1
)))
fit=lm(Day15pseudoneutid50_BA.1~Bpseudoneutid50_BA.1+I(Bpseudoneutid50_BA.1^2), subset(tmp, Day15pseudoneutid50_BA.1>1))
tmp$pred=predict(fit,newdata=tmp)
plot(Day15pseudoneutid50_BA.1~Bpseudoneutid50_BA.1, tmp, xlim=c(0,5), ylim=c(0,5), asp=1); mylines(tmp$Bpseudoneutid50_BA.1, tmp$pred, col=2)
coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ Day15pseudoneutid50_BA.1 + naive, tmp)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## Day15pseudoneutid50_BA.1 + naive, data = tmp)
##
## coef exp(coef) se(coef) z p
## Day15pseudoneutid50_BA.1 -0.3955 0.6733 0.2953 -1.339 0.1804
## naive 2.0235 7.5650 1.0372 1.951 0.0511
##
## Likelihood ratio test=11.53 on 2 df, p=0.003133
## n= 96, number of events= 25coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ Bpseudoneutid50_BA.1 + naive, tmp)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## Bpseudoneutid50_BA.1 + naive, data = tmp)
##
## coef exp(coef) se(coef) z p
## Bpseudoneutid50_BA.1 -0.4027 0.6685 0.2733 -1.473 0.1406
## naive 1.8507 6.3645 1.0584 1.749 0.0804
##
## Likelihood ratio test=12.14 on 2 df, p=0.002311
## n= 96, number of events= 25coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ pred + naive, tmp)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## pred + naive, data = tmp)
##
## coef exp(coef) se(coef) z p
## pred -0.6858 0.5037 0.5333 -1.286 0.1985
## naive 1.9690 7.1639 1.0474 1.880 0.0601
##
## Likelihood ratio test=11.55 on 2 df, p=0.003112
## n= 96, number of events= 25For manuscript revision
compute median value for each category
tmp=subset(dat_proc, ph1.D15==1 & TrtonedosemRNA==1 & Day15pseudoneutid50_BA.1 <= 3.44762) # naive==1 &
10**(wtd.quantile(tmp$Day15pseudoneutid50_BA.1, weights = tmp$wt.D15, probs = c(1/2)))## 50%
## 1358.5tmp=subset(dat_proc, ph1.D15==1 & TrtonedosemRNA==1 & Day15pseudoneutid50_BA.1 > 3.44762 & Day15pseudoneutid50_BA.1 <= 3.95012) # & naive==1
10**(wtd.quantile(tmp$Day15pseudoneutid50_BA.1, weights = tmp$wt.D15, probs = c(1/2)))## 50%
## 4830.996tmp=subset(dat_proc, ph1.D15==1 & TrtonedosemRNA==1 & Day15pseudoneutid50_BA.1 > 3.95012) # & naive==1
10**(wtd.quantile(tmp$Day15pseudoneutid50_BA.1, weights = tmp$wt.D15, probs = c(1/2)))## 50%
## 17121Relationship between risk score and age
plot(dat_proc$Age, dat_proc$risk_score)
Difference in risk between prototype and Omicon vaccines
Summary: Between Pfz Prototype, Pfz Omicron, Mdn P, and Mdn O,
- Pfizer O has lower risk than the other three arms.
- Moderna P is the only arm in terms of BA4BA5 ID50 not being a correlate.
Risk is similar between Pfizer and Moderna in the prototype arms. Moderna P and O have similar risks, but Pfizer O has lower risk than P.
coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ company, subset(dat.ocp,naive==1 & Trt==0))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## company, data = subset(dat.ocp, naive == 1 & Trt == 0))
##
## coef exp(coef) se(coef) z p
## companyPfz 0.4122 1.5101 0.3472 1.187 0.235
##
## Likelihood ratio test=1.35 on 1 df, p=0.2454
## n= 105, number of events= 36coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ standardized_risk_score + company + Day15pseudoneutid50_BA.4.BA.5 + FOIstandardized, subset(dat.ocp,naive==1 & Trt==1))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## standardized_risk_score + company + Day15pseudoneutid50_BA.4.BA.5 +
## FOIstandardized, data = subset(dat.ocp, naive == 1 &
## Trt == 1))
##
## coef exp(coef) se(coef) z p
## standardized_risk_score 0.4680 1.5969 0.1666 2.810 0.004958
## companyPfz -0.9440 0.3891 0.3578 -2.638 0.008329
## Day15pseudoneutid50_BA.4.BA.5 -0.7299 0.4819 0.2006 -3.639 0.000273
## FOIstandardized -0.1906 0.8264 0.2324 -0.820 0.412125
##
## Likelihood ratio test=27.65 on 4 df, p=1.469e-05
## n= 213, number of events= 57coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ Trt, subset(dat.ocp,naive==1 & company=="Pfz"))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## Trt, data = subset(dat.ocp, naive == 1 & company == "Pfz"))
##
## coef exp(coef) se(coef) z p
## Trt -1.238 0.290 0.422 -2.933 0.00336
##
## Likelihood ratio test=8.54 on 1 df, p=0.003482
## n= 98, number of events= 23coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ Trt, subset(dat.ocp,naive==1 & company=="Mdn"))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## Trt, data = subset(dat.ocp, naive == 1 & company == "Mdn"))
##
## coef exp(coef) se(coef) z p
## Trt -0.02566 0.97466 0.25475 -0.101 0.92
##
## Likelihood ratio test=0.01 on 1 df, p=0.9199
## n= 220, number of events= 70coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ Trt * company, subset(dat.ocp, naive==1))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## Trt * company, data = subset(dat.ocp, naive == 1))
##
## coef exp(coef) se(coef) z p
## Trt -0.0218 0.9784 0.2547 -0.086 0.9318
## companyPfz 0.4226 1.5259 0.3471 1.218 0.2234
## Trt:companyPfz -1.2309 0.2920 0.4917 -2.503 0.0123
##
## Likelihood ratio test=10.39 on 3 df, p=0.01553
## n= 318, number of events= 93coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ I(1-Trt) * company, subset(dat.ocp, naive==1))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## I(1 - Trt) * company, data = subset(dat.ocp, naive == 1))
##
## coef exp(coef) se(coef) z p
## I(1 - Trt) 0.0218 1.0220 0.2547 0.086 0.9318
## companyPfz -0.8083 0.4456 0.3483 -2.320 0.0203
## I(1 - Trt):companyPfz 1.2309 3.4242 0.4917 2.503 0.0123
##
## Likelihood ratio test=10.39 on 3 df, p=0.01553
## n= 318, number of events= 93Pfizer
OC v P is significant among the naive but not among the non-naive.
coxph(update(f, ~. + naive + Trt), dat.ocp.pfizer)## Call:
## coxph(formula = update(f, ~. + naive + Trt), data = dat.ocp.pfizer)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.1126 0.8935 0.2984 -0.377 0.70591
## standardized_risk_score 0.2795 1.3224 0.2878 0.971 0.33154
## naive 0.8310 2.2955 0.4551 1.826 0.06787
## Trt -1.1399 0.3199 0.3749 -3.040 0.00236
##
## Likelihood ratio test=12.14 on 4 df, p=0.01631
## n= 151, number of events= 30coxph(update(f, ~. + Trt), subset(dat.ocp.pfizer, naive==0))## Call:
## coxph(formula = update(f, ~. + Trt), data = subset(dat.ocp.pfizer,
## naive == 0))
##
## coef exp(coef) se(coef) z p
## FOIstandardized 0.59531 1.81359 0.61480 0.968 0.333
## standardized_risk_score 0.04664 1.04775 0.52826 0.088 0.930
## Trt -0.52997 0.58862 0.77255 -0.686 0.493
##
## Likelihood ratio test=1.38 on 3 df, p=0.7098
## n= 53, number of events= 7coxph(update(f, ~. + Trt), subset(dat.ocp.pfizer, naive==1))## Call:
## coxph(formula = update(f, ~. + Trt), data = subset(dat.ocp.pfizer,
## naive == 1))
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3243 0.7231 0.3318 -0.977 0.3285
## standardized_risk_score 0.4130 1.5113 0.3592 1.150 0.2503
## Trt -1.3346 0.2633 0.4359 -3.062 0.0022
##
## Likelihood ratio test=10.59 on 3 df, p=0.01415
## n= 98, number of events= 23coxph(update(f, ~. + naive + Trt), dat.ocp.moderna)## Call:
## coxph(formula = update(f, ~. + naive + Trt), data = dat.ocp.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.1298 0.8782 0.1923 -0.675 0.49956
## standardized_risk_score 0.2056 1.2283 0.1441 1.427 0.15360
## naive 1.0871 2.9657 0.3741 2.906 0.00367
## Trt -0.1180 0.8887 0.2388 -0.494 0.62124
##
## Likelihood ratio test=13.62 on 4 df, p=0.008612
## n= 284, number of events= 78coxph(update(f, ~. + Trt), subset(dat.ocp.moderna, naive==0))## Call:
## coxph(formula = update(f, ~. + Trt), data = subset(dat.ocp.moderna,
## naive == 0))
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.4871 0.6144 0.5445 -0.895 0.3710
## standardized_risk_score -0.4855 0.6154 0.2100 -2.312 0.0208
## Trt -0.5147 0.5977 0.7306 -0.705 0.4811
##
## Likelihood ratio test=6.87 on 3 df, p=0.07628
## n= 64, number of events= 8coxph(update(f, ~. + Trt), subset(dat.ocp.moderna, naive==1))## Call:
## coxph(formula = update(f, ~. + Trt), data = subset(dat.ocp.moderna,
## naive == 1))
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.03082 0.96965 0.20833 -0.148 0.8824
## standardized_risk_score 0.45535 1.57672 0.16296 2.794 0.0052
## Trt -0.07904 0.92400 0.25598 -0.309 0.7575
##
## Likelihood ratio test=8.48 on 3 df, p=0.03705
## n= 220, number of events= 70coxph(update(f, ~. + Trt), subset(dat.ocp.moderna, naive==1))## Call:
## coxph(formula = update(f, ~. + Trt), data = subset(dat.ocp.moderna,
## naive == 1))
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.03082 0.96965 0.20833 -0.148 0.8824
## standardized_risk_score 0.45535 1.57672 0.16296 2.794 0.0052
## Trt -0.07904 0.92400 0.25598 -0.309 0.7575
##
## Likelihood ratio test=8.48 on 3 df, p=0.03705
## n= 220, number of events= 70coxph(update(f, ~. + Trt), subset(dat.ocp.pfizer, naive==1))## Call:
## coxph(formula = update(f, ~. + Trt), data = subset(dat.ocp.pfizer,
## naive == 1))
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3243 0.7231 0.3318 -0.977 0.3285
## standardized_risk_score 0.4130 1.5113 0.3592 1.150 0.2503
## Trt -1.3346 0.2633 0.4359 -3.062 0.0022
##
## Likelihood ratio test=10.59 on 3 df, p=0.01415
## n= 98, number of events= 23coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ I(1-Trt)*scale(standardized_risk_score), subset(dat.ocp.moderna, naive==1))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## I(1 - Trt) * scale(standardized_risk_score), data = subset(dat.ocp.moderna,
## naive == 1))
##
## coef exp(coef) se(coef) z
## I(1 - Trt) 0.1259 1.1341 0.2626 0.479
## scale(standardized_risk_score) 0.4569 1.5792 0.1567 2.917
## I(1 - Trt):scale(standardized_risk_score) -0.3079 0.7350 0.2860 -1.077
## p
## I(1 - Trt) 0.63173
## scale(standardized_risk_score) 0.00354
## I(1 - Trt):scale(standardized_risk_score) 0.28163
##
## Likelihood ratio test=9.58 on 3 df, p=0.02251
## n= 220, number of events= 70coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ I(1-Trt)*scale(standardized_risk_score), subset(dat.ocp.pfizer, naive==1))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## I(1 - Trt) * scale(standardized_risk_score), data = subset(dat.ocp.pfizer,
## naive == 1))
##
## coef exp(coef) se(coef) z
## I(1 - Trt) 1.334775 3.799141 0.439440 3.037
## scale(standardized_risk_score) 0.252246 1.286913 0.302894 0.833
## I(1 - Trt):scale(standardized_risk_score) -0.003043 0.996962 0.513319 -0.006
## p
## I(1 - Trt) 0.00239
## scale(standardized_risk_score) 0.40496
## I(1 - Trt):scale(standardized_risk_score) 0.99527
##
## Likelihood ratio test=9.64 on 3 df, p=0.02186
## n= 98, number of events= 23D15 marker
coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ I(1-Trt)*scale(standardized_risk_score) + I(1-Trt)*scale(Day15pseudoneutid50_BA.4.BA.5), subset(dat.ocp.moderna, naive==1))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## I(1 - Trt) * scale(standardized_risk_score) + I(1 - Trt) *
## scale(Day15pseudoneutid50_BA.4.BA.5), data = subset(dat.ocp.moderna,
## naive == 1))
##
## coef exp(coef) se(coef)
## I(1 - Trt) 0.1114 1.1178 0.2661
## scale(standardized_risk_score) 0.4149 1.5142 0.1504
## scale(Day15pseudoneutid50_BA.4.BA.5) -0.4160 0.6597 0.1256
## I(1 - Trt):scale(standardized_risk_score) -0.2653 0.7670 0.2835
## I(1 - Trt):scale(Day15pseudoneutid50_BA.4.BA.5) 0.4191 1.5206 0.2328
## z p
## I(1 - Trt) 0.419 0.675561
## scale(standardized_risk_score) 2.759 0.005804
## scale(Day15pseudoneutid50_BA.4.BA.5) -3.311 0.000929
## I(1 - Trt):scale(standardized_risk_score) -0.936 0.349342
## I(1 - Trt):scale(Day15pseudoneutid50_BA.4.BA.5) 1.801 0.071746
##
## Likelihood ratio test=19.05 on 5 df, p=0.001884
## n= 220, number of events= 70coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ I(1-Trt)*scale(standardized_risk_score) + I(1-Trt)*scale(Day15pseudoneutid50_BA.4.BA.5), subset(dat.ocp.pfizer, naive==1))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## I(1 - Trt) * scale(standardized_risk_score) + I(1 - Trt) *
## scale(Day15pseudoneutid50_BA.4.BA.5), data = subset(dat.ocp.pfizer,
## naive == 1))
##
## coef exp(coef) se(coef)
## I(1 - Trt) 1.22319 3.39801 0.47177
## scale(standardized_risk_score) 0.25727 1.29339 0.28060
## scale(Day15pseudoneutid50_BA.4.BA.5) -0.39649 0.67268 0.30594
## I(1 - Trt):scale(standardized_risk_score) -0.07131 0.93117 0.51280
## I(1 - Trt):scale(Day15pseudoneutid50_BA.4.BA.5) 0.08472 1.08841 0.48874
## z p
## I(1 - Trt) 2.593 0.00952
## scale(standardized_risk_score) 0.917 0.35923
## scale(Day15pseudoneutid50_BA.4.BA.5) -1.296 0.19498
## I(1 - Trt):scale(standardized_risk_score) -0.139 0.88940
## I(1 - Trt):scale(Day15pseudoneutid50_BA.4.BA.5) 0.173 0.86239
##
## Likelihood ratio test=11.86 on 5 df, p=0.03673
## n= 98, number of events= 23coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ Trt*scale(standardized_risk_score) + Trt*scale(Day15pseudoneutid50_BA.4.BA.5), subset(dat.ocp.moderna, naive==1))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## Trt * scale(standardized_risk_score) + Trt * scale(Day15pseudoneutid50_BA.4.BA.5),
## data = subset(dat.ocp.moderna, naive == 1))
##
## coef exp(coef) se(coef) z
## Trt -0.111365 0.894612 0.266089 -0.419
## scale(standardized_risk_score) 0.149640 1.161417 0.240360 0.623
## scale(Day15pseudoneutid50_BA.4.BA.5) 0.003167 1.003172 0.195995 0.016
## Trt:scale(standardized_risk_score) 0.265277 1.303791 0.283455 0.936
## Trt:scale(Day15pseudoneutid50_BA.4.BA.5) -0.419130 0.657619 0.232756 -1.801
## p
## Trt 0.6756
## scale(standardized_risk_score) 0.5336
## scale(Day15pseudoneutid50_BA.4.BA.5) 0.9871
## Trt:scale(standardized_risk_score) 0.3493
## Trt:scale(Day15pseudoneutid50_BA.4.BA.5) 0.0717
##
## Likelihood ratio test=19.05 on 5 df, p=0.001884
## n= 220, number of events= 70coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ Trt*scale(standardized_risk_score) + Trt*scale(Day15pseudoneutid50_BA.4.BA.5), subset(dat.ocp.pfizer, naive==1))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## Trt * scale(standardized_risk_score) + Trt * scale(Day15pseudoneutid50_BA.4.BA.5),
## data = subset(dat.ocp.pfizer, naive == 1))
##
## coef exp(coef) se(coef) z
## Trt -1.22319 0.29429 0.47177 -2.593
## scale(standardized_risk_score) 0.18595 1.20437 0.42922 0.433
## scale(Day15pseudoneutid50_BA.4.BA.5) -0.31177 0.73215 0.38065 -0.819
## Trt:scale(standardized_risk_score) 0.07131 1.07392 0.51280 0.139
## Trt:scale(Day15pseudoneutid50_BA.4.BA.5) -0.08472 0.91877 0.48874 -0.173
## p
## Trt 0.00952
## scale(standardized_risk_score) 0.66484
## scale(Day15pseudoneutid50_BA.4.BA.5) 0.41276
## Trt:scale(standardized_risk_score) 0.88940
## Trt:scale(Day15pseudoneutid50_BA.4.BA.5) 0.86239
##
## Likelihood ratio test=11.86 on 5 df, p=0.03673
## n= 98, number of events= 23coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ Trt*scale(standardized_risk_score) + scale(Day15pseudoneutid50_BA.4.BA.5), subset(dat.ocp.moderna, naive==1))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## Trt * scale(standardized_risk_score) + scale(Day15pseudoneutid50_BA.4.BA.5),
## data = subset(dat.ocp.moderna, naive == 1))
##
## coef exp(coef) se(coef) z p
## Trt 0.01104 1.01111 0.26824 0.041 0.96716
## scale(standardized_risk_score) 0.19188 1.21152 0.23255 0.825 0.40932
## scale(Day15pseudoneutid50_BA.4.BA.5) -0.27822 0.75713 0.10780 -2.581 0.00986
## Trt:scale(standardized_risk_score) 0.23881 1.26974 0.27919 0.855 0.39234
##
## Likelihood ratio test=15.7 on 4 df, p=0.003451
## n= 220, number of events= 70coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ Trt+scale(standardized_risk_score) + scale(Day15pseudoneutid50_BA.4.BA.5), subset(dat.ocp.pfizer, naive==1))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## Trt + scale(standardized_risk_score) + scale(Day15pseudoneutid50_BA.4.BA.5),
## data = subset(dat.ocp.pfizer, naive == 1))
##
## coef exp(coef) se(coef) z p
## Trt -1.1912 0.3039 0.4390 -2.713 0.00666
## scale(standardized_risk_score) 0.2285 1.2567 0.2312 0.988 0.32313
## scale(Day15pseudoneutid50_BA.4.BA.5) -0.3558 0.7006 0.2317 -1.536 0.12463
##
## Likelihood ratio test=11.81 on 3 df, p=0.008078
## n= 98, number of events= 23BA4BA5-specific ID50 is significant correlate but not ancestral ID50.
coxph(update(f, ~. + Trt * Bpseudoneutid50_BA.4.BA.5), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt * Bpseudoneutid50_BA.4.BA.5),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.37008 0.69068 0.32778 -1.129 0.259
## standardized_risk_score 0.46192 1.58712 0.35856 1.288 0.198
## Trt -1.45196 0.23411 1.39782 -1.039 0.299
## Bpseudoneutid50_BA.4.BA.5 -0.76476 0.46545 0.51612 -1.482 0.138
## Trt:Bpseudoneutid50_BA.4.BA.5 0.02565 1.02598 0.75166 0.034 0.973
##
## Likelihood ratio test=14.86 on 5 df, p=0.011
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3707 0.6903 0.3275 -1.132 0.25778
## standardized_risk_score 0.4619 1.5871 0.3584 1.289 0.19754
## Trt -1.4067 0.2449 0.4402 -3.196 0.00139
## Bpseudoneutid50_BA.4.BA.5 -0.7528 0.4711 0.3771 -1.996 0.04593
##
## Likelihood ratio test=14.85 on 4 df, p=0.005012
## n= 98, number of events= 23coxph(update(f, ~. + Trt * Day15pseudoneutid50_BA.4.BA.5), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt * Day15pseudoneutid50_BA.4.BA.5),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3928 0.6751 0.3376 -1.163 0.245
## standardized_risk_score 0.4077 1.5033 0.3387 1.204 0.229
## Trt -0.5003 0.6063 2.3862 -0.210 0.834
## Day15pseudoneutid50_BA.4.BA.5 -0.5345 0.5860 0.6290 -0.850 0.395
## Trt:Day15pseudoneutid50_BA.4.BA.5 -0.2392 0.7873 0.8374 -0.286 0.775
##
## Likelihood ratio test=13.19 on 5 df, p=0.0217
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Day15pseudoneutid50_BA.4.BA.5), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Day15pseudoneutid50_BA.4.BA.5),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3846 0.6807 0.3365 -1.143 0.25298
## standardized_risk_score 0.3880 1.4741 0.3346 1.160 0.24613
## Trt -1.1710 0.3101 0.4380 -2.673 0.00751
## Day15pseudoneutid50_BA.4.BA.5 -0.6690 0.5122 0.4026 -1.661 0.09662
##
## Likelihood ratio test=13.1 on 4 df, p=0.01078
## n= 98, number of events= 23coxph(update(f, ~. + Trt * Bpseudoneutid50_D614G), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt * Bpseudoneutid50_D614G),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3129 0.7313 0.3325 -0.941 0.347
## standardized_risk_score 0.3846 1.4690 0.3593 1.070 0.284
## Trt -2.5917 0.0749 3.0261 -0.856 0.392
## Bpseudoneutid50_D614G -0.7526 0.4711 0.6990 -1.077 0.282
## Trt:Bpseudoneutid50_D614G 0.3747 1.4545 0.9066 0.413 0.679
##
## Likelihood ratio test=12.25 on 5 df, p=0.03156
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Bpseudoneutid50_D614G), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_D614G),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3433 0.7094 0.3265 -1.051 0.29307
## standardized_risk_score 0.4042 1.4981 0.3542 1.141 0.25378
## Trt -1.3536 0.2583 0.4357 -3.106 0.00189
## Bpseudoneutid50_D614G -0.5220 0.5934 0.4159 -1.255 0.20949
##
## Likelihood ratio test=12.07 on 4 df, p=0.01682
## n= 98, number of events= 23coxph(update(f, ~. + Trt * Day15pseudoneutid50_D614G), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt * Day15pseudoneutid50_D614G),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.34880 0.70554 0.33352 -1.046 0.296
## standardized_risk_score 0.37937 1.46136 0.33824 1.122 0.262
## Trt 2.10109 8.17504 5.09592 0.412 0.680
## Day15pseudoneutid50_D614G 0.06733 1.06965 0.98947 0.068 0.946
## Trt:Day15pseudoneutid50_D614G -0.81890 0.44092 1.22190 -0.670 0.503
##
## Likelihood ratio test=11.66 on 5 df, p=0.03977
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Day15pseudoneutid50_D614G), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Day15pseudoneutid50_D614G),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3568 0.6999 0.3320 -1.075 0.28252
## standardized_risk_score 0.3775 1.4586 0.3485 1.083 0.27878
## Trt -1.3000 0.2725 0.4364 -2.979 0.00289
## Day15pseudoneutid50_D614G -0.4664 0.6273 0.5907 -0.789 0.42983
##
## Likelihood ratio test=11.21 on 4 df, p=0.02432
## n= 98, number of events= 23Distribution of D15 BA4BA5-specific ID50 is different between P and OC
par(mfrow=c(1,2))
myboxplot(Bpseudoneutid50_BA.4.BA.5~Trt, dat.1, test="w")
myboxplot(Day15pseudoneutid50_BA.4.BA.5~Trt, dat.1, test="w")
Additional marker models
coxph(update(f, ~. + Trt + Bpseudoneutid50_D614G + Day15pseudoneutid50_D614G + I(Day15pseudoneutid50_D614G^2)), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_D614G +
## Day15pseudoneutid50_D614G + I(Day15pseudoneutid50_D614G^2)),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3455 0.7079 0.3285 -1.052 0.29297
## standardized_risk_score 0.3851 1.4697 0.3798 1.014 0.31063
## Trt -1.3295 0.2646 0.4678 -2.842 0.00449
## Bpseudoneutid50_D614G -0.4862 0.6150 0.4908 -0.991 0.32187
## Day15pseudoneutid50_D614G 0.7199 2.0541 9.1491 0.079 0.93729
## I(Day15pseudoneutid50_D614G^2) -0.0983 0.9064 1.0962 -0.090 0.92855
##
## Likelihood ratio test=12.1 on 6 df, p=0.05974
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Bpseudoneutid50_D614G * Day15pseudoneutid50_D614G), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_D614G *
## Day15pseudoneutid50_D614G), data = dat.1)
##
## coef exp(coef) se(coef)
## FOIstandardized -0.3629 0.6956 0.3266
## standardized_risk_score 0.3633 1.4380 0.3727
## Trt -1.2999 0.2725 0.4475
## Bpseudoneutid50_D614G 2.9004 18.1816 6.1345
## Day15pseudoneutid50_D614G 2.4931 12.0987 4.7804
## Bpseudoneutid50_D614G:Day15pseudoneutid50_D614G -0.8016 0.4486 1.4502
## z p
## FOIstandardized -1.111 0.26650
## standardized_risk_score 0.975 0.32977
## Trt -2.905 0.00368
## Bpseudoneutid50_D614G 0.473 0.63635
## Day15pseudoneutid50_D614G 0.522 0.60200
## Bpseudoneutid50_D614G:Day15pseudoneutid50_D614G -0.553 0.58043
##
## Likelihood ratio test=12.44 on 6 df, p=0.05286
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Bpseudoneutid50_D614G + Day15pseudoneutid50_D614G), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_D614G +
## Day15pseudoneutid50_D614G), data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.34752 0.70644 0.32782 -1.060 0.28910
## standardized_risk_score 0.39723 1.48770 0.35421 1.121 0.26210
## Trt -1.34393 0.26082 0.44034 -3.052 0.00227
## Bpseudoneutid50_D614G -0.48573 0.61525 0.49278 -0.986 0.32428
## Day15pseudoneutid50_D614G -0.09788 0.90675 0.68958 -0.142 0.88712
##
## Likelihood ratio test=12.09 on 5 df, p=0.03353
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Bpseudoneutid50_D614G), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_D614G),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3433 0.7094 0.3265 -1.051 0.29307
## standardized_risk_score 0.4042 1.4981 0.3542 1.141 0.25378
## Trt -1.3536 0.2583 0.4357 -3.106 0.00189
## Bpseudoneutid50_D614G -0.5220 0.5934 0.4159 -1.255 0.20949
##
## Likelihood ratio test=12.07 on 4 df, p=0.01682
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Day15pseudoneutid50_D614G), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Day15pseudoneutid50_D614G),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3568 0.6999 0.3320 -1.075 0.28252
## standardized_risk_score 0.3775 1.4586 0.3485 1.083 0.27878
## Trt -1.3000 0.2725 0.4364 -2.979 0.00289
## Day15pseudoneutid50_D614G -0.4664 0.6273 0.5907 -0.789 0.42983
##
## Likelihood ratio test=11.21 on 4 df, p=0.02432
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5 + Day15pseudoneutid50_BA.4.BA.5 + I(Day15pseudoneutid50_BA.4.BA.5^2)), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5 +
## Day15pseudoneutid50_BA.4.BA.5 + I(Day15pseudoneutid50_BA.4.BA.5^2)),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.39082 0.67650 0.33397 -1.170 0.24192
## standardized_risk_score 0.44598 1.56201 0.35217 1.266 0.20538
## Trt -1.32656 0.26539 0.48196 -2.752 0.00592
## Bpseudoneutid50_BA.4.BA.5 -0.63623 0.52928 0.44876 -1.418 0.15626
## Day15pseudoneutid50_BA.4.BA.5 -0.46895 0.62566 2.24326 -0.209 0.83441
## I(Day15pseudoneutid50_BA.4.BA.5^2) 0.03931 1.04009 0.41458 0.095 0.92446
##
## Likelihood ratio test=15.12 on 6 df, p=0.01931
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5 * Day15pseudoneutid50_BA.4.BA.5), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5 *
## Day15pseudoneutid50_BA.4.BA.5), data = dat.1)
##
## coef exp(coef)
## FOIstandardized -0.3963 0.6728
## standardized_risk_score 0.4624 1.5879
## Trt -1.3413 0.2615
## Bpseudoneutid50_BA.4.BA.5 -1.5970 0.2025
## Day15pseudoneutid50_BA.4.BA.5 -0.7192 0.4871
## Bpseudoneutid50_BA.4.BA.5:Day15pseudoneutid50_BA.4.BA.5 0.3065 1.3587
## se(coef) z p
## FOIstandardized 0.3337 -1.188 0.23499
## standardized_risk_score 0.3541 1.306 0.19161
## Trt 0.4739 -2.830 0.00465
## Bpseudoneutid50_BA.4.BA.5 2.8898 -0.553 0.58053
## Day15pseudoneutid50_BA.4.BA.5 1.4386 -0.500 0.61712
## Bpseudoneutid50_BA.4.BA.5:Day15pseudoneutid50_BA.4.BA.5 0.9028 0.340 0.73419
##
## Likelihood ratio test=15.23 on 6 df, p=0.01856
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5 + Day15pseudoneutid50_BA.4.BA.5), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5 +
## Day15pseudoneutid50_BA.4.BA.5), data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3932 0.6749 0.3327 -1.182 0.2373
## standardized_risk_score 0.4418 1.5555 0.3494 1.264 0.2061
## Trt -1.3153 0.2684 0.4664 -2.820 0.0048
## Bpseudoneutid50_BA.4.BA.5 -0.6318 0.5316 0.4472 -1.413 0.1577
## Day15pseudoneutid50_BA.4.BA.5 -0.2616 0.7698 0.5056 -0.517 0.6049
##
## Likelihood ratio test=15.12 on 5 df, p=0.009877
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3707 0.6903 0.3275 -1.132 0.25778
## standardized_risk_score 0.4619 1.5871 0.3584 1.289 0.19754
## Trt -1.4067 0.2449 0.4402 -3.196 0.00139
## Bpseudoneutid50_BA.4.BA.5 -0.7528 0.4711 0.3771 -1.996 0.04593
##
## Likelihood ratio test=14.85 on 4 df, p=0.005012
## n= 98, number of events= 23coxph(update(f, ~. + Trt + Day15pseudoneutid50_BA.4.BA.5), dat.1)## Call:
## coxph(formula = update(f, ~. + Trt + Day15pseudoneutid50_BA.4.BA.5),
## data = dat.1)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3846 0.6807 0.3365 -1.143 0.25298
## standardized_risk_score 0.3880 1.4741 0.3346 1.160 0.24613
## Trt -1.1710 0.3101 0.4380 -2.673 0.00751
## Day15pseudoneutid50_BA.4.BA.5 -0.6690 0.5122 0.4026 -1.661 0.09662
##
## Likelihood ratio test=13.1 on 4 df, p=0.01078
## n= 98, number of events= 23Moderna
No treatment effect in either naive or non-naive.
coxph(update(f, ~. + naive + Trt), dat.ocp.moderna)## Call:
## coxph(formula = update(f, ~. + naive + Trt), data = dat.ocp.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.1298 0.8782 0.1923 -0.675 0.49956
## standardized_risk_score 0.2056 1.2283 0.1441 1.427 0.15360
## naive 1.0871 2.9657 0.3741 2.906 0.00367
## Trt -0.1180 0.8887 0.2388 -0.494 0.62124
##
## Likelihood ratio test=13.62 on 4 df, p=0.008612
## n= 284, number of events= 78coxph(update(f, ~. + naive + Trt), subset(dat.ocp.moderna, naive==0))## Call:
## coxph(formula = update(f, ~. + naive + Trt), data = subset(dat.ocp.moderna,
## naive == 0))
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.4871 0.6144 0.5445 -0.895 0.3710
## standardized_risk_score -0.4855 0.6154 0.2100 -2.312 0.0208
## naive NA NA 0.0000 NA NA
## Trt -0.5147 0.5977 0.7306 -0.705 0.4811
##
## Likelihood ratio test=6.87 on 3 df, p=0.07628
## n= 64, number of events= 8coxph(update(f, ~. + naive + Trt), subset(dat.ocp.moderna, naive==1))## Call:
## coxph(formula = update(f, ~. + naive + Trt), data = subset(dat.ocp.moderna,
## naive == 1))
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.03082 0.96965 0.20833 -0.148 0.8824
## standardized_risk_score 0.45535 1.57672 0.16296 2.794 0.0052
## naive NA NA 0.00000 NA NA
## Trt -0.07904 0.92400 0.25598 -0.309 0.7575
##
## Likelihood ratio test=8.48 on 3 df, p=0.03705
## n= 220, number of events= 70Distribution of D15 BA4BA5-specific ID50 is different between P and OC
par(mfrow=c(1,2))
myboxplot(Bpseudoneutid50_BA.4.BA.5~Trt, dat.1.moderna, test="w")
myboxplot(Day15pseudoneutid50_BA.4.BA.5~Trt, dat.1.moderna, test="w")
Among naive, there is significant interaction between Trt and D1 BA4BA5 ID50
coxph(update(f, ~. + Trt * Bpseudoneutid50_BA.4.BA.5), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt * Bpseudoneutid50_BA.4.BA.5),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.10876 0.89694 0.21194 -0.513 0.60782
## standardized_risk_score 0.51757 1.67794 0.17018 3.041 0.00236
## Trt 1.43400 4.19546 0.80039 1.792 0.07319
## Bpseudoneutid50_BA.4.BA.5 -0.03369 0.96687 0.31280 -0.108 0.91422
## Trt:Bpseudoneutid50_BA.4.BA.5 -0.78765 0.45491 0.38980 -2.021 0.04332
##
## Likelihood ratio test=22.09 on 5 df, p=0.0005028
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.08466 0.91883 0.21020 -0.403 0.6871
## standardized_risk_score 0.52343 1.68781 0.16853 3.106 0.0019
## Trt -0.05426 0.94719 0.25656 -0.211 0.8325
## Bpseudoneutid50_BA.4.BA.5 -0.55763 0.57256 0.18591 -3.000 0.0027
##
## Likelihood ratio test=18.02 on 4 df, p=0.001224
## n= 220, number of events= 70coxph(update(f, ~. + Trt * Day15pseudoneutid50_BA.4.BA.5), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt * Day15pseudoneutid50_BA.4.BA.5),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.063658 0.938326 0.208384 -0.305 0.76000
## standardized_risk_score 0.419372 1.521006 0.158592 2.644 0.00818
## Trt 2.167095 8.732879 1.238928 1.749 0.08026
## Day15pseudoneutid50_BA.4.BA.5 -0.006817 0.993206 0.346598 -0.020 0.98431
## Trt:Day15pseudoneutid50_BA.4.BA.5 -0.725744 0.483964 0.409206 -1.774 0.07614
##
## Likelihood ratio test=18.29 on 5 df, p=0.002603
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Day15pseudoneutid50_BA.4.BA.5), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Day15pseudoneutid50_BA.4.BA.5),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.04024 0.96056 0.20792 -0.194 0.84655
## standardized_risk_score 0.44264 1.55682 0.15841 2.794 0.00520
## Trt 0.05208 1.05347 0.26166 0.199 0.84222
## Day15pseudoneutid50_BA.4.BA.5 -0.49862 0.60737 0.18590 -2.682 0.00731
##
## Likelihood ratio test=15.02 on 4 df, p=0.004658
## n= 220, number of events= 70coxph(update(f, ~. + Trt * Bpseudoneutid50_D614G), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt * Bpseudoneutid50_D614G),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.01409 0.98600 0.21342 -0.066 0.94734
## standardized_risk_score 0.48600 1.62579 0.16721 2.906 0.00366
## Trt 1.57583 4.83477 1.56471 1.007 0.31388
## Bpseudoneutid50_D614G -0.22904 0.79530 0.40054 -0.572 0.56745
## Trt:Bpseudoneutid50_D614G -0.48992 0.61268 0.46328 -1.058 0.29028
##
## Likelihood ratio test=17.22 on 5 df, p=0.004097
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Bpseudoneutid50_D614G), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_D614G),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized 0.005631 1.005647 0.211626 0.027 0.97877
## standardized_risk_score 0.489978 1.632280 0.166749 2.938 0.00330
## Trt -0.042098 0.958776 0.256407 -0.164 0.86959
## Bpseudoneutid50_D614G -0.591856 0.553299 0.210497 -2.812 0.00493
##
## Likelihood ratio test=16.12 on 4 df, p=0.002862
## n= 220, number of events= 70coxph(update(f, ~. + Trt * Day15pseudoneutid50_D614G), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt * Day15pseudoneutid50_D614G),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.04257 0.95833 0.21104 -0.202 0.84015
## standardized_risk_score 0.42967 1.53675 0.16333 2.631 0.00852
## Trt 2.75942 15.79061 2.68283 1.029 0.30369
## Day15pseudoneutid50_D614G -0.33609 0.71456 0.52728 -0.637 0.52386
## Trt:Day15pseudoneutid50_D614G -0.67350 0.50992 0.63901 -1.054 0.29190
##
## Likelihood ratio test=16.55 on 5 df, p=0.005445
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Day15pseudoneutid50_D614G), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Day15pseudoneutid50_D614G),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.02905 0.97137 0.20991 -0.138 0.88992
## standardized_risk_score 0.43341 1.54250 0.16353 2.650 0.00804
## Trt -0.04242 0.95847 0.25644 -0.165 0.86862
## Day15pseudoneutid50_D614G -0.78976 0.45395 0.29689 -2.660 0.00781
##
## Likelihood ratio test=15.42 on 4 df, p=0.003899
## n= 220, number of events= 70Additional marker models
coxph(update(f, ~. + Trt + Bpseudoneutid50_D614G + Day15pseudoneutid50_D614G + I(Day15pseudoneutid50_D614G^2)), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_D614G +
## Day15pseudoneutid50_D614G + I(Day15pseudoneutid50_D614G^2)),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -5.814e-03 9.942e-01 2.114e-01 -0.028 0.9781
## standardized_risk_score 4.733e-01 1.605e+00 1.694e-01 2.794 0.0052
## Trt -3.537e-02 9.653e-01 2.567e-01 -0.138 0.8904
## Bpseudoneutid50_D614G -4.341e-01 6.478e-01 2.870e-01 -1.513 0.1304
## Day15pseudoneutid50_D614G 9.463e+00 1.287e+04 6.180e+00 1.531 0.1257
## I(Day15pseudoneutid50_D614G^2) -1.195e+00 3.028e-01 7.478e-01 -1.598 0.1101
##
## Likelihood ratio test=20.2 on 6 df, p=0.00255
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Bpseudoneutid50_D614G * Day15pseudoneutid50_D614G), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_D614G *
## Day15pseudoneutid50_D614G), data = dat.1.moderna)
##
## coef exp(coef) se(coef)
## FOIstandardized -0.02009 0.98011 0.21239
## standardized_risk_score 0.47970 1.61559 0.16983
## Trt -0.04103 0.95980 0.25641
## Bpseudoneutid50_D614G 3.60636 36.83171 2.59976
## Day15pseudoneutid50_D614G 2.52712 12.51740 1.94240
## Bpseudoneutid50_D614G:Day15pseudoneutid50_D614G -0.94366 0.38920 0.61038
## z p
## FOIstandardized -0.095 0.92463
## standardized_risk_score 2.825 0.00473
## Trt -0.160 0.87285
## Bpseudoneutid50_D614G 1.387 0.16538
## Day15pseudoneutid50_D614G 1.301 0.19325
## Bpseudoneutid50_D614G:Day15pseudoneutid50_D614G -1.546 0.12210
##
## Likelihood ratio test=19.83 on 6 df, p=0.002965
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Bpseudoneutid50_D614G + Day15pseudoneutid50_D614G), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_D614G +
## Day15pseudoneutid50_D614G), data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.007339 0.992688 0.211648 -0.035 0.9723
## standardized_risk_score 0.462311 1.587738 0.167240 2.764 0.0057
## Trt -0.031277 0.969207 0.256769 -0.122 0.9030
## Bpseudoneutid50_D614G -0.395893 0.673078 0.293522 -1.349 0.1774
## Day15pseudoneutid50_D614G -0.406311 0.666103 0.404493 -1.004 0.3151
##
## Likelihood ratio test=17.16 on 5 df, p=0.004213
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Bpseudoneutid50_D614G), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_D614G),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized 0.005631 1.005647 0.211626 0.027 0.97877
## standardized_risk_score 0.489978 1.632280 0.166749 2.938 0.00330
## Trt -0.042098 0.958776 0.256407 -0.164 0.86959
## Bpseudoneutid50_D614G -0.591856 0.553299 0.210497 -2.812 0.00493
##
## Likelihood ratio test=16.12 on 4 df, p=0.002862
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Day15pseudoneutid50_D614G), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Day15pseudoneutid50_D614G),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.02905 0.97137 0.20991 -0.138 0.88992
## standardized_risk_score 0.43341 1.54250 0.16353 2.650 0.00804
## Trt -0.04242 0.95847 0.25644 -0.165 0.86862
## Day15pseudoneutid50_D614G -0.78976 0.45395 0.29689 -2.660 0.00781
##
## Likelihood ratio test=15.42 on 4 df, p=0.003899
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5 + Day15pseudoneutid50_BA.4.BA.5 + I(Day15pseudoneutid50_BA.4.BA.5^2)), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5 +
## Day15pseudoneutid50_BA.4.BA.5 + I(Day15pseudoneutid50_BA.4.BA.5^2)),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.16507 0.84784 0.21337 -0.774 0.43916
## standardized_risk_score 0.45693 1.57922 0.16953 2.695 0.00703
## Trt 0.09869 1.10373 0.26505 0.372 0.70963
## Bpseudoneutid50_BA.4.BA.5 -0.18224 0.83340 0.24952 -0.730 0.46517
## Day15pseudoneutid50_BA.4.BA.5 4.87481 130.94983 1.87375 2.602 0.00928
## I(Day15pseudoneutid50_BA.4.BA.5^2) -0.97889 0.37573 0.34700 -2.821 0.00479
##
## Likelihood ratio test=29.29 on 6 df, p=5.372e-05
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5 * Day15pseudoneutid50_BA.4.BA.5), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5 *
## Day15pseudoneutid50_BA.4.BA.5), data = dat.1.moderna)
##
## coef exp(coef)
## FOIstandardized -0.13156 0.87673
## standardized_risk_score 0.45336 1.57359
## Trt 0.04978 1.05104
## Bpseudoneutid50_BA.4.BA.5 5.66331 288.10120
## Day15pseudoneutid50_BA.4.BA.5 2.57487 13.12966
## Bpseudoneutid50_BA.4.BA.5:Day15pseudoneutid50_BA.4.BA.5 -1.87482 0.15338
## se(coef) z
## FOIstandardized 0.21151 -0.622
## standardized_risk_score 0.16885 2.685
## Trt 0.26683 0.187
## Bpseudoneutid50_BA.4.BA.5 1.70177 3.328
## Day15pseudoneutid50_BA.4.BA.5 0.84488 3.048
## Bpseudoneutid50_BA.4.BA.5:Day15pseudoneutid50_BA.4.BA.5 0.53345 -3.515
## p
## FOIstandardized 0.533957
## standardized_risk_score 0.007252
## Trt 0.851993
## Bpseudoneutid50_BA.4.BA.5 0.000875
## Day15pseudoneutid50_BA.4.BA.5 0.002307
## Bpseudoneutid50_BA.4.BA.5:Day15pseudoneutid50_BA.4.BA.5 0.000441
##
## Likelihood ratio test=32.41 on 6 df, p=1.363e-05
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5 + Day15pseudoneutid50_BA.4.BA.5), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5 +
## Day15pseudoneutid50_BA.4.BA.5), data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.076829 0.926048 0.209936 -0.366 0.71439
## standardized_risk_score 0.501798 1.651689 0.167333 2.999 0.00271
## Trt -0.002618 0.997386 0.264350 -0.010 0.99210
## Bpseudoneutid50_BA.4.BA.5 -0.445376 0.640583 0.233371 -1.908 0.05633
## Day15pseudoneutid50_BA.4.BA.5 -0.201700 0.817340 0.246766 -0.817 0.41371
##
## Likelihood ratio test=18.67 on 5 df, p=0.002214
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Bpseudoneutid50_BA.4.BA.5),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.08466 0.91883 0.21020 -0.403 0.6871
## standardized_risk_score 0.52343 1.68781 0.16853 3.106 0.0019
## Trt -0.05426 0.94719 0.25656 -0.211 0.8325
## Bpseudoneutid50_BA.4.BA.5 -0.55763 0.57256 0.18591 -3.000 0.0027
##
## Likelihood ratio test=18.02 on 4 df, p=0.001224
## n= 220, number of events= 70coxph(update(f, ~. + Trt + Day15pseudoneutid50_BA.4.BA.5), dat.1.moderna)## Call:
## coxph(formula = update(f, ~. + Trt + Day15pseudoneutid50_BA.4.BA.5),
## data = dat.1.moderna)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.04024 0.96056 0.20792 -0.194 0.84655
## standardized_risk_score 0.44264 1.55682 0.15841 2.794 0.00520
## Trt 0.05208 1.05347 0.26166 0.199 0.84222
## Day15pseudoneutid50_BA.4.BA.5 -0.49862 0.60737 0.18590 -2.682 0.00731
##
## Likelihood ratio test=15.02 on 4 df, p=0.004658
## n= 220, number of events= 70coxph(update(f, ~. + Day15pseudoneutid50_BA.4.BA.5), subset(dat.1.moderna, Trt==1))## Call:
## coxph(formula = update(f, ~. + Day15pseudoneutid50_BA.4.BA.5),
## data = subset(dat.1.moderna, Trt == 1))
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.1627 0.8498 0.2699 -0.603 0.546488
## standardized_risk_score 0.4981 1.6456 0.1865 2.670 0.007581
## Day15pseudoneutid50_BA.4.BA.5 -0.7579 0.4686 0.2176 -3.484 0.000495
##
## Likelihood ratio test=19.88 on 3 df, p=0.0001794
## n= 145, number of events= 47coxph(update(f, ~. + Day15pseudoneutid50_D614G), subset(dat.1.moderna, Trt==1))## Call:
## coxph(formula = update(f, ~. + Day15pseudoneutid50_D614G), data = subset(dat.1.moderna,
## Trt == 1))
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.1406 0.8688 0.2749 -0.512 0.60893
## standardized_risk_score 0.5205 1.6829 0.1945 2.676 0.00745
## Day15pseudoneutid50_D614G -1.0358 0.3549 0.3641 -2.845 0.00444
##
## Likelihood ratio test=17.49 on 3 df, p=0.000561
## n= 145, number of events= 47Putting Pfizer and Moderna side by side
Omicron-specific antibodies are similar at D1, rise at D15, and more so when vaccines contain Omicron strains.
par(mfrow=c(1,2))
myboxplot(Bpseudoneutid50_BA.4.BA.5~Trt+company, subset(dat.ocp, naive==1), cex.axis=.8)
myboxplot(Day15pseudoneutid50_BA.4.BA.5~Trt+company, subset(dat.ocp, naive==1), cex.axis=.8)
Ancestral antibodies are not quite similar at D1, rise at D15, but not more so when vaccines contain Omicron strains.
par(mfrow=c(1,2))
myboxplot(Bpseudoneutid50_D614G~Trt+company, subset(dat.ocp, naive==1), cex.axis=.8)
myboxplot(Day15pseudoneutid50_D614G~Trt+company, subset(dat.ocp, naive==1), cex.axis=.8)
fits=list()
fits[["Pfz,P"]] = coxph(update(f, ~. + Day15pseudoneutid50_D614G), subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0))
fits[["Pfz,O"]] = coxph(update(f, ~. + Day15pseudoneutid50_D614G), subset(dat.ocp, naive==1 & company=="Pfz" & Trt==1))
fits[["Mdn,P"]] = coxph(update(f, ~. + Day15pseudoneutid50_D614G), subset(dat.ocp, naive==1 & company=="Mdn" & Trt==0))
fits[["Mdn,O"]] = coxph(update(f, ~. + Day15pseudoneutid50_D614G), subset(dat.ocp, naive==1 & company=="Mdn" & Trt==1))
tab = getFormattedSummary(fits, robust=F, type=5)
tab## Pfz,P Pfz,O Mdn,P Mdn,O
## FOIstandardized "-0.49" "-0.22" "0.19" "-0.14"
## standardized_risk_score "0.36" "0.36" "0.15" "0.52**"
## Day15pseudoneutid50_D614G "-0.15" "-0.70" "-0.37" "-1.04**"Are risk scores distribution different?
myboxplot(standardized_risk_score ~Trt+company, subset(dat.ocp, naive==1), cex.axis=.8)
coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ standardized_risk_score*Trt, subset(dat.ocp, naive==1 & company=="Mdn"))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## standardized_risk_score * Trt, data = subset(dat.ocp, naive ==
## 1 & company == "Mdn"))
##
## coef exp(coef) se(coef) z p
## standardized_risk_score 0.1836 1.2015 0.2949 0.623 0.534
## Trt -0.2121 0.8089 0.2839 -0.747 0.455
## standardized_risk_score:Trt 0.3793 1.4613 0.3523 1.077 0.282
##
## Likelihood ratio test=9.58 on 3 df, p=0.02251
## n= 220, number of events= 70BA4BA5 marker. Three out of the four effect sizes are comparable to each other, but in the Moderna prototype arm there is no association between BA4BA5 marker and risk. This is quite intriguing - if we view each column above as a correlates analysis, we have three positive results and one negative result.
fits=list()
fits[["Pfz,P"]] = coxph(update(f, ~. + Day15pseudoneutid50_BA.4.BA.5), subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0))
fits[["Pfz,O"]] = coxph(update(f, ~. + Day15pseudoneutid50_BA.4.BA.5), subset(dat.ocp, naive==1 & company=="Pfz" & Trt==1))
fits[["Mdn,P"]] = coxph(update(f, ~. + Day15pseudoneutid50_BA.4.BA.5), subset(dat.ocp, naive==1 & company=="Mdn" & Trt==0))
fits[["Mdn,O"]] = coxph(update(f, ~. + Day15pseudoneutid50_BA.4.BA.5), subset(dat.ocp, naive==1 & company=="Mdn" & Trt==1))
tab = getFormattedSummary(fits, robust=F, type=5)
tab## Pfz,P Pfz,O Mdn,P Mdn,O
## FOIstandardized "-0.51" "-0.29" "0.17" "-0.16"
## standardized_risk_score "0.25" "0.44" "0.16" "0.50**"
## Day15pseudoneutid50_BA.4.BA.5 "-0.62" "-0.75" "0.00" "-0.76**"Correlation between ancestral and BA4BA5 ID50s in Pfz,P and Pfz,O separately
par(mfrow=c(1,2))
lim=c(2.5,5.3)
corplot(Day15pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_D614G, subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0), asp=1, xlim=lim, ylim=lim, main="Pfz, Prototype")
corplot(Day15pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_D614G, subset(dat.ocp, naive==1 & company=="Pfz" & Trt==1), asp=1, xlim=lim, ylim=lim, main="Pfz, Omicron")
Risk models
ID50 Score
dat$D1_id50=scale(dat$Bpseudoneutid50_MDW,scale=F)
dat$Day15_id50=scale(dat$Day15pseudoneutid50_MDW,scale=F)
coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ FOIstandardized + standardized_risk_score + naive + Day15_id50, dat)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## FOIstandardized + standardized_risk_score + naive + Day15_id50,
## data = dat)
##
## coef exp(coef) se(coef) z p
## FOIstandardized 0.001851 1.001853 0.082550 0.022 0.98211
## standardized_risk_score 0.220804 1.247079 0.084345 2.618 0.00885
## naive 1.054369 2.870165 0.210874 5.000 5.73e-07
## Day15_id50 -0.371085 0.689986 0.134860 -2.752 0.00593
##
## Likelihood ratio test=77.26 on 4 df, p=6.61e-16
## n= 985, number of events= 213coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ standardized_risk_score + naive + D1_id50 + Day15_id50 + I(Day15_id50^2), dat)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## standardized_risk_score + naive + D1_id50 + Day15_id50 +
## I(Day15_id50^2), data = dat)
##
## coef exp(coef) se(coef) z p
## standardized_risk_score 0.24172 1.27343 0.08176 2.956 0.003112
## naive 0.66527 1.94501 0.22308 2.982 0.002862
## D1_id50 -0.44490 0.64089 0.16269 -2.735 0.006245
## Day15_id50 -0.49043 0.61236 0.25055 -1.957 0.050294
## I(Day15_id50^2) -0.98915 0.37189 0.27479 -3.600 0.000319
##
## Likelihood ratio test=103.4 on 5 df, p=< 2.2e-16
## n= 985, number of events= 213coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ standardized_risk_score + naive + naive*(D1_id50 + Day15_id50 + I(Day15_id50^2)), dat)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## standardized_risk_score + naive + naive * (D1_id50 + Day15_id50 +
## I(Day15_id50^2)), data = dat)
##
## coef exp(coef) se(coef) z p
## standardized_risk_score 0.23644 1.26673 0.08174 2.892 0.00382
## naive 0.56240 1.75488 0.22961 2.449 0.01431
## D1_id50 -0.91307 0.40129 0.36906 -2.474 0.01336
## Day15_id50 -0.76230 0.46659 0.56949 -1.339 0.18071
## I(Day15_id50^2) -0.75581 0.46963 0.46669 -1.620 0.10534
## naive:D1_id50 0.56603 1.76126 0.40890 1.384 0.16628
## naive:Day15_id50 0.33588 1.39917 0.63396 0.530 0.59624
## naive:I(Day15_id50^2) -0.13493 0.87377 0.55767 -0.242 0.80881
##
## Likelihood ratio test=108 on 8 df, p=< 2.2e-16
## n= 985, number of events= 213coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ standardized_risk_score + naive + naive*D1_id50 + Day15_id50 + I(Day15_id50^2), dat)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## standardized_risk_score + naive + naive * D1_id50 + Day15_id50 +
## I(Day15_id50^2), data = dat)
##
## coef exp(coef) se(coef) z p
## standardized_risk_score 0.23504 1.26496 0.08166 2.878 0.003998
## naive 0.53717 1.71115 0.21176 2.537 0.011192
## D1_id50 -1.02753 0.35789 0.30465 -3.373 0.000744
## Day15_id50 -0.47621 0.62113 0.24694 -1.928 0.053804
## I(Day15_id50^2) -0.88168 0.41409 0.26717 -3.300 0.000966
## naive:D1_id50 0.70604 2.02595 0.32593 2.166 0.030292
##
## Likelihood ratio test=107.7 on 6 df, p=< 2.2e-16
## n= 985, number of events= 213coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ standardized_risk_score + naive + D1_id50 + naive*Day15_id50 + naive*I(Day15_id50^2), dat)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## standardized_risk_score + naive + D1_id50 + naive * Day15_id50 +
## naive * I(Day15_id50^2), data = dat)
##
## coef exp(coef) se(coef) z p
## standardized_risk_score 0.24273 1.27472 0.08174 2.969 0.00298
## naive 0.63995 1.89639 0.23269 2.750 0.00596
## D1_id50 -0.44191 0.64281 0.16069 -2.750 0.00596
## Day15_id50 -1.19176 0.30368 0.48297 -2.468 0.01360
## I(Day15_id50^2) -0.75693 0.46911 0.45572 -1.661 0.09673
## naive:Day15_id50 0.84626 2.33092 0.51659 1.638 0.10139
## naive:I(Day15_id50^2) -0.14280 0.86693 0.54910 -0.260 0.79482
##
## Likelihood ratio test=106.2 on 7 df, p=< 2.2e-16
## n= 985, number of events= 213coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ standardized_risk_score + naive + D1_id50 + Day15_id50 + naive*I(Day15_id50^2), dat)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## standardized_risk_score + naive + D1_id50 + Day15_id50 +
## naive * I(Day15_id50^2), data = dat)
##
## coef exp(coef) se(coef) z p
## standardized_risk_score 0.24081 1.27228 0.08177 2.945 0.00323
## naive 0.70910 2.03216 0.24324 2.915 0.00355
## D1_id50 -0.44461 0.64108 0.16272 -2.732 0.00629
## Day15_id50 -0.52303 0.59272 0.26075 -2.006 0.04487
## I(Day15_id50^2) -0.70683 0.49321 0.62738 -1.127 0.25990
## naive:I(Day15_id50^2) -0.34038 0.71150 0.71376 -0.477 0.63345
##
## Likelihood ratio test=103.6 on 6 df, p=< 2.2e-16
## n= 985, number of events= 213coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ standardized_risk_score + naive + D1_id50 + naive*Day15_id50 + I(Day15_id50^2), dat)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## standardized_risk_score + naive + D1_id50 + naive * Day15_id50 +
## I(Day15_id50^2), data = dat)
##
## coef exp(coef) se(coef) z p
## standardized_risk_score 0.24300 1.27507 0.08171 2.974 0.00294
## naive 0.61857 1.85626 0.21646 2.858 0.00427
## D1_id50 -0.44083 0.64350 0.16055 -2.746 0.00604
## Day15_id50 -1.21649 0.29627 0.48543 -2.506 0.01221
## I(Day15_id50^2) -0.86024 0.42306 0.26191 -3.285 0.00102
## naive:Day15_id50 0.88834 2.43109 0.50261 1.767 0.07715
##
## Likelihood ratio test=106.2 on 6 df, p=< 2.2e-16
## n= 985, number of events= 213ID50 BA4BA5
dat$D1_id50=scale(dat$Bpseudoneutid50_BA.4.BA.5,scale=F)
dat$Day15_id50=scale(dat$Day15pseudoneutid50_BA.4.BA.5,scale=F)
coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ standardized_risk_score + naive + naive*D1_id50 + Day15_id50 + I(Day15_id50^2), dat)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## standardized_risk_score + naive + naive * D1_id50 + Day15_id50 +
## I(Day15_id50^2), data = dat)
##
## coef exp(coef) se(coef) z p
## standardized_risk_score 0.2190 1.2448 0.0811 2.700 0.00694
## naive 0.4651 1.5921 0.2136 2.177 0.02946
## D1_id50 -0.9630 0.3818 0.2799 -3.441 0.00058
## Day15_id50 -0.4177 0.6586 0.2126 -1.964 0.04948
## I(Day15_id50^2) -0.4346 0.6475 0.1563 -2.779 0.00545
## naive:D1_id50 0.6896 1.9930 0.2910 2.370 0.01779
##
## Likelihood ratio test=106.1 on 6 df, p=< 2.2e-16
## n= 985, number of events= 213# filter out those with undetectable D1
coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ standardized_risk_score + naive + naive*D1_id50 + Day15_id50 + I(Day15_id50^2), subset(dat, D1_id50 > -1.06))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## standardized_risk_score + naive + naive * D1_id50 + Day15_id50 +
## I(Day15_id50^2), data = subset(dat, D1_id50 > -1.06))
##
## coef exp(coef) se(coef) z p
## standardized_risk_score 0.0757 1.0786 0.1045 0.724 0.4688
## naive 0.4559 1.5776 0.3107 1.467 0.1423
## D1_id50 -0.9408 0.3903 0.4848 -1.941 0.0523
## Day15_id50 -0.6463 0.5240 0.2964 -2.180 0.0292
## I(Day15_id50^2) -0.6404 0.5271 0.3936 -1.627 0.1037
## naive:D1_id50 0.8220 2.2751 0.5404 1.521 0.1282
##
## Likelihood ratio test=63.51 on 6 df, p=8.688e-12
## n= 699, number of events= 117# fit model to get inference for D1_id50 in the naive
coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ standardized_risk_score + I(1-naive)*D1_id50 + Day15_id50 + I(Day15_id50^2), dat)## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## standardized_risk_score + I(1 - naive) * D1_id50 + Day15_id50 +
## I(Day15_id50^2), data = dat)
##
## coef exp(coef) se(coef) z p
## standardized_risk_score 0.2190 1.2448 0.0811 2.700 0.00694
## I(1 - naive) -0.4651 0.6281 0.2136 -2.177 0.02946
## D1_id50 -0.2733 0.7608 0.1396 -1.958 0.05027
## Day15_id50 -0.4177 0.6586 0.2126 -1.964 0.04948
## I(Day15_id50^2) -0.4346 0.6475 0.1563 -2.779 0.00545
## I(1 - naive):D1_id50 -0.6896 0.5018 0.2910 -2.370 0.01779
##
## Likelihood ratio test=106.1 on 6 df, p=< 2.2e-16
## n= 985, number of events= 213# filter out those with undetectable D1
coxph(Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ standardized_risk_score + I(1-naive)*D1_id50 + Day15_id50 + I(Day15_id50^2), subset(dat, D1_id50 > -1.06))## Call:
## coxph(formula = Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~
## standardized_risk_score + I(1 - naive) * D1_id50 + Day15_id50 +
## I(Day15_id50^2), data = subset(dat, D1_id50 > -1.06))
##
## coef exp(coef) se(coef) z p
## standardized_risk_score 0.0757 1.0786 0.1045 0.724 0.4688
## I(1 - naive) -0.4559 0.6339 0.3107 -1.467 0.1423
## D1_id50 -0.1188 0.8880 0.3228 -0.368 0.7128
## Day15_id50 -0.6463 0.5240 0.2964 -2.180 0.0292
## I(Day15_id50^2) -0.6404 0.5271 0.3936 -1.627 0.1037
## I(1 - naive):D1_id50 -0.8220 0.4395 0.5404 -1.521 0.1282
##
## Likelihood ratio test=63.51 on 6 df, p=8.688e-12
## n= 699, number of events= 117The effect of risk score is different between models with and without double naive (naive and with undetectable D1_id50).
dat$D1_id50=scale(dat$Bpseudoneutid50_BA.4.BA.5,scale=F)
myboxplot(standardized_risk_score~I(1-naive)*I(D1_id50 > -1.06), dat, ylab="standardized_risk_score", names=c("N, D1 undet", "NN, D1 undet", "N, D1 det", "NN, D1 det"))
fit=lm(standardized_risk_score ~ I(1-naive) * I(D1_id50 > -1.06), dat); summary(fit)##
## Call:
## lm(formula = standardized_risk_score ~ I(1 - naive) * I(D1_id50 >
## -1.06), data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.5176 -0.5421 0.1354 0.6626 2.0105
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03930 0.06026 0.652 0.514516
## I(1 - naive) 0.58424 0.29421 1.986 0.047336 *
## I(D1_id50 > -1.06)TRUE 0.13356 0.08022 1.665 0.096237 .
## I(1 - naive):I(D1_id50 > -1.06)TRUE -1.15269 0.30374 -3.795 0.000157 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9976 on 981 degrees of freedom
## Multiple R-squared: 0.06359, Adjusted R-squared: 0.06073
## F-statistic: 22.21 on 3 and 981 DF, p-value: 6.459e-14fit=lm(standardized_risk_score ~ I(1-naive), subset(dat, D1_id50 <= -1.06)); summary(fit)##
## Call:
## lm(formula = standardized_risk_score ~ I(1 - naive), data = subset(dat,
## D1_id50 <= -1.06))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2614 -0.4698 0.1859 0.5916 1.9658
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0393 0.0567 0.693 0.4888
## I(1 - naive) 0.5842 0.2768 2.111 0.0357 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9385 on 284 degrees of freedom
## Multiple R-squared: 0.01545, Adjusted R-squared: 0.01198
## F-statistic: 4.455 on 1 and 284 DF, p-value: 0.03566f=Surv(COVIDtimeD22toD181, COVIDIndD22toD181) ~ FOIstandardized + standardized_risk_score
dat.ocp2=subset(dat_proc, ph1.D15==1 & treatment_assigned %in% c(
"Wildtype/Prototype (Pfizer 1)"
, "Omicron (Pfizer 1)"
, "Omicron + Wildtype/Prototype (Pfizer 1)"
# , "Omicron BA.4/5 + Prototype (Pfizer 2)"
# , "Omicron BA.1 + Prototype (Pfizer 2)"
)
)
dat.ocp2$Trt=ifelse(dat.ocp2$treatment_assigned == "Wildtype/Prototype (Pfizer 1)", 0, 1)
coxph(update(f, ~. + naive + Trt), dat.ocp2)## Call:
## coxph(formula = update(f, ~. + naive + Trt), data = dat.ocp2)
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.1126 0.8935 0.2984 -0.377 0.70591
## standardized_risk_score 0.2795 1.3224 0.2878 0.971 0.33154
## naive 0.8310 2.2955 0.4551 1.826 0.06787
## Trt -1.1399 0.3199 0.3749 -3.040 0.00236
##
## Likelihood ratio test=12.14 on 4 df, p=0.01631
## n= 151, number of events= 30coxph(update(f, ~. + naive + Trt), subset(dat.ocp2, naive==0))## Call:
## coxph(formula = update(f, ~. + naive + Trt), data = subset(dat.ocp2,
## naive == 0))
##
## coef exp(coef) se(coef) z p
## FOIstandardized 0.59531 1.81359 0.61480 0.968 0.333
## standardized_risk_score 0.04664 1.04775 0.52826 0.088 0.930
## naive NA NA 0.00000 NA NA
## Trt -0.52997 0.58862 0.77255 -0.686 0.493
##
## Likelihood ratio test=1.38 on 3 df, p=0.7098
## n= 53, number of events= 7coxph(update(f, ~. + naive + Trt), subset(dat.ocp2, naive==1))## Call:
## coxph(formula = update(f, ~. + naive + Trt), data = subset(dat.ocp2,
## naive == 1))
##
## coef exp(coef) se(coef) z p
## FOIstandardized -0.3243 0.7231 0.3318 -0.977 0.3285
## standardized_risk_score 0.4130 1.5113 0.3592 1.150 0.2503
## naive NA NA 0.0000 NA NA
## Trt -1.3346 0.2633 0.4359 -3.062 0.0022
##
## Likelihood ratio test=10.59 on 3 df, p=0.01415
## n= 98, number of events= 23dat.1=subset(dat.ocp2, naive==1)B and D15 marker
table(dat$Bpseudoneutid50_MDWcat, dat$Day15pseudoneutid50_MDWcat)##
## (-Inf,3.62] (3.62,4.07] (4.07, Inf]
## (-Inf,2.56] 228 80 20
## (2.56,3.27] 94 155 80
## (3.27, Inf] 6 94 228corplot(Day15pseudoneutid50_MDW~Bpseudoneutid50_MDW, dat, col=ifelse(dat$naive==1,1,2))
# fit demming regression
dat.n=subset(dat, naive==1)
dat.nn=subset(dat, naive==0)
fit.n = Deming(dat.n$Bpseudoneutid50_MDW, dat.n$Day15pseudoneutid50_MDW, boot = TRUE)
fit.nn = Deming(dat.nn$Bpseudoneutid50_MDW, dat.nn$Day15pseudoneutid50_MDW, boot = TRUE)
summary(fit.n)## est se(est) (lower upper)
## Intercept 1.7871909 0.09110302 1.6060820 1.9576747
## Slope 0.7210016 0.03387975 0.6587173 0.7917871
## sigma.dat.n$Bpseudoneutid50_MDW 0.2985262 0.01199177 0.2758940 0.3226616
## sigma.dat.n$Day15pseudoneutid50_MDW 0.2985262 0.01199177 0.2758940 0.3226616
## p.value
## Intercept 1.101506e-85
## Slope 1.695729e-100
## sigma.dat.n$Bpseudoneutid50_MDW 8.584826e-137
## sigma.dat.n$Day15pseudoneutid50_MDW 8.584826e-137summary(fit.nn)## est se(est) (lower upper)
## Intercept 1.5126849 0.23922704 1.0462625 1.9694045
## Slope 0.7497295 0.06739409 0.6212233 0.8797335
## sigma.dat.nn$Bpseudoneutid50_MDW 0.2761024 0.01648200 0.2426575 0.3060327
## sigma.dat.nn$Day15pseudoneutid50_MDW 0.2761024 0.01648200 0.2426575 0.3060327
## p.value
## Intercept 2.561708e-10
## Slope 9.527661e-29
## sigma.dat.nn$Bpseudoneutid50_MDW 5.497849e-63
## sigma.dat.nn$Day15pseudoneutid50_MDW 5.497849e-63par(mfrow=c(2,1), mar=c(3,4,2,2))
lim=range(c(dat$Bpseudoneutid50_MDW, dat$Day15pseudoneutid50_MDW))
corplot(Day15pseudoneutid50_MDW~Bpseudoneutid50_MDW, dat.n, add.deming.fit = T, xlim=lim, ylim=lim, xlab="", main="Naive", method="pearson")
corplot(Day15pseudoneutid50_MDW~Bpseudoneutid50_MDW, dat.nn, add.deming.fit = T, xlim=lim, ylim=lim, main="Non-naive", method="pearson")
title(xlab="Bpseudoneutid50_MDW", line=2)
distribution
table(dat$Bpseudoneutid50_MDWcat, dat$Day15pseudoneutid50_MDWcat)##
## (-Inf,3.62] (3.62,4.07] (4.07, Inf]
## (-Inf,2.56] 228 80 20
## (2.56,3.27] 94 155 80
## (3.27, Inf] 6 94 228table(dat$Bpseudoneutid50_MDWcat, dat$Day15pseudoneutid50_MDWcat, dat$naive)## , , = 0
##
##
## (-Inf,3.62] (3.62,4.07] (4.07, Inf]
## (-Inf,2.56] 11 6 3
## (2.56,3.27] 24 42 26
## (3.27, Inf] 5 67 172
##
## , , = 1
##
##
## (-Inf,3.62] (3.62,4.07] (4.07, Inf]
## (-Inf,2.56] 217 74 17
## (2.56,3.27] 70 113 54
## (3.27, Inf] 1 27 56# all cases have covid lineage observed
mytable(dat_proc$COVIDlineageObserved, dat_proc$COVIDlineage)##
## BA.2 BA.4 BA.5 XBB.1.5 XBB<ca>0.10 XZ <NA>
## FALSE 23 0 83 13 0 0 0
## TRUE 62 24 161 15 1 1 0
## <NA> 0 0 0 0 0 0 857mytable(dat_proc$COVIDlineageObserved, dat_proc$COVIDIndD22toend)##
## 0 1 <NA>
## FALSE 36 83 0
## TRUE 60 180 24
## <NA> 857 0 0save the risk score from covail_data_processed_20240205.csv as a separate file that will be treated as the source of risk score
dat1=read.csv("/trials/covpn/COVAILcorrelates/analysis/correlates/adata/covail_data_processed_20240205.csv")
write.csv(subset(dat1, select=c(Ptid, risk_score, standardized_risk_score)), row.names=F, file="/trials/covpn/COVAILcorrelates/analysis/correlates/adata/risk_score.csv")There is a difference in risk score between 0205 and 0206 analysis ready datasets
dat1=read.csv("/trials/covpn/COVAILcorrelates/analysis/mapping_immune_correlates/adata/covail_mapped_data_20240205.csv")
dat2=read.csv("/trials/covpn/COVAILcorrelates/analysis/mapping_immune_correlates/adata/covail_mapped_data_20240206.csv")
nrow(dat1)
nrow(dat2)
cbind(dat1$COVIDIndD22toD181, dat2$COVIDIndD22toD181)[1:100,]
dat1=read.csv("/trials/covpn/COVAILcorrelates/analysis/correlates/adata/covail_data_processed_20240205.csv")
dat2=read.csv("/trials/covpn/COVAILcorrelates/analysis/correlates/adata/covail_data_processed_20240206.csv")
dat3=read.csv("/trials/covpn/COVAILcorrelates/analysis/correlates/adata/covail_data_processed_20240211.csv")
nrow(dat1)
nrow(dat2)
nrow(dat3)
with(subset(dat1,ph1.D15==1), fastauc(risk_score, COVIDIndD22toD181))
with(subset(dat2,ph1.D15==1), fastauc(risk_score, COVIDIndD22toD181))
dat1=read.csv("/trials/covpn/COVAILcorrelates/analysis/mapping_immune_correlates/adata/covail_mapped_data_20240208.csv")
dat2=read.csv("/trials/covpn/COVAILcorrelates/analysis/mapping_immune_correlates/adata/covail_mapped_data_20240211.csv")
rbind(dat1[16,-182],dat2[16,]) # COVIDtimeD22toend
setdiff(names(dat1), names(dat2))
mytable(dat2$COVIDIndD92toD181, dat2$COVIDtimeD92toD181)sanofi markers
with(dat_mapped, table(treatment_actual %in% c("Beta (Sanofi)", "Beta + Prototype (Sanofi)", "Prototype (Sanofi)"), TrtSanofi))## TrtSanofi
## 0 1
## FALSE 1110 0
## TRUE 0 152summary(dat.sanofi[,c("Day29"%.%assays[1:5])])## Day29pseudoneutid50_D614G Day29pseudoneutid50_Delta Day29pseudoneutid50_Beta
## Min. :2.964 Min. :2.761 Min. :2.450
## 1st Qu.:3.747 1st Qu.:3.542 1st Qu.:3.413
## Median :4.127 Median :3.846 Median :3.813
## Mean :4.118 Mean :3.892 Mean :3.821
## 3rd Qu.:4.457 3rd Qu.:4.260 3rd Qu.:4.238
## Max. :5.195 Max. :5.036 Max. :5.136
## NA's :6 NA's :6 NA's :6
## Day29pseudoneutid50_BA.1 Day29pseudoneutid50_BA.4.BA.5
## Min. :1.301 Min. :1.301
## 1st Qu.:2.769 1st Qu.:2.693
## Median :3.322 Median :3.155
## Mean :3.226 Mean :3.091
## 3rd Qu.:3.720 3rd Qu.:3.543
## Max. :4.865 Max. :4.507
## NA's :6 NA's :6summary(dat.sanofi[,c("Day15"%.%assays[1:5])])## Day15pseudoneutid50_D614G Day15pseudoneutid50_Delta Day15pseudoneutid50_Beta
## Min. :2.830 Min. :2.537 Min. :2.290
## 1st Qu.:3.657 1st Qu.:3.325 1st Qu.:3.257
## Median :3.922 Median :3.612 Median :3.611
## Mean :3.971 Mean :3.657 Mean :3.633
## 3rd Qu.:4.278 3rd Qu.:3.982 3rd Qu.:4.028
## Max. :4.929 Max. :4.733 Max. :4.913
## NA's :6 NA's :6 NA's :6
## Day15pseudoneutid50_BA.1 Day15pseudoneutid50_BA.4.BA.5
## Min. :1.301 Min. :1.301
## 1st Qu.:2.732 1st Qu.:2.618
## Median :3.191 Median :3.021
## Mean :3.204 Mean :3.002
## 3rd Qu.:3.612 3rd Qu.:3.489
## Max. :4.612 Max. :4.493
## NA's :6 NA's :6with(subset(dat_mapped, TrtSanofi==1), mytable(naive, COVIDIndD36toD181))## COVIDIndD36toD181
## naive 0 1 <NA>
## 0 56 4 2
## 1 66 13 11with(subset(dat_mapped, TrtSanofi==1), mytable(naive, COVIDIndD22toD181))## COVIDIndD22toD181
## naive 0 1 <NA>
## 0 56 4 2
## 1 65 18 7Correlation is high between BA4BA5 ID50 and ancestral ID50 in both naive and nnaive (higher in the latter)
par(mfrow=c(1,2))
lim=c(2.5,5.3)
corplot(Day15pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_D614G, subset(dat.sanofi, naive==1), asp=1, xlim=lim, ylim=lim, main="COVAIL Sanofi Naive")
corplot(Day15pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_D614G, dat.sanofi, asp=1, xlim=lim, ylim=lim, main="COVAIL Sanofi")
# corplot(Day15pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_D614G, subset(dat.sanofi, naive==0), asp=1, xlim=lim, ylim=lim)Comparing with Pfz,P in the naive
par(mfrow=c(2,2))
lim=c(1.2,5.5)
myboxplot(list(subset(dat.sanofi, naive==1, Day15pseudoneutid50_D614G, drop=T),
subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0,Day15pseudoneutid50_D614G, drop=T)), test="w", main="D15 Ancestral ID50", ylim=lim, names=c("Sanofi, naive", "Pfz,P, naive"), ylab="")
myboxplot(list(subset(dat.sanofi, naive==1, Day15pseudoneutid50_BA.4.BA.5, drop=T),
subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0,Day15pseudoneutid50_BA.4.BA.5, drop=T)), test="w", main="D15 BA4BA5 ID50", ylim=lim, names=c("Sanofi, naive", "Pfz,P, naive"), ylab="")
corplot(Day15pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_D614G, subset(dat.sanofi, naive==1), asp=1, xlim=lim, ylim=lim, main="Sanofi Naive")
corplot(Day15pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_D614G, subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0, c(Day15pseudoneutid50_D614G, Day15pseudoneutid50_BA.4.BA.5)), asp=1, xlim=lim, ylim=lim, main="Pfz,P Naive")
Comparing with Pfz,P in the naive, separately by Sanofi,P and Sanofi,NP
par(mfrow=c(1,3))
corplot(Day15pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_D614G, subset(dat.sanofi, naive==1 & treatment_actual=="Prototype (Sanofi)"), asp=1, xlim=lim, ylim=lim, main="Sanofi,P Naive")
corplot(Day15pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_D614G, subset(dat.sanofi, naive==1 & treatment_actual!="Prototype (Sanofi)"), asp=1, xlim=lim, ylim=lim, main="Sanofi,NP Naive")
corplot(Day15pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_D614G, subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0, c(Day15pseudoneutid50_D614G, Day15pseudoneutid50_BA.4.BA.5)), asp=1, xlim=lim, ylim=lim, main="Pfz,P Naive")
Comparing with Pfz,P in the non-naive
par(mfrow=c(2,2))
lim=c(1.2,5.5)
myboxplot(list(subset(dat.sanofi, naive==0, Day15pseudoneutid50_D614G, drop=T),
subset(dat.ocp, naive==0 & company=="Pfz" & Trt==0,Day15pseudoneutid50_D614G, drop=T)), test="w", main="D15 Ancestral ID50", ylim=lim, names=c("Sanofi, Non-naive", "Pfz,P, Non-naive"), ylab="")
myboxplot(list(subset(dat.sanofi, naive==0, Day15pseudoneutid50_BA.4.BA.5, drop=T),
subset(dat.ocp, naive==0 & company=="Pfz" & Trt==0,Day15pseudoneutid50_BA.4.BA.5, drop=T)), test="w", main="D15 BA4BA5 ID50", ylim=lim, names=c("Sanofi, Non-naive", "Pfz,P, Non-naive"), ylab="")
corplot(Day15pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_D614G, subset(dat.sanofi, naive==0), asp=1, xlim=lim, ylim=lim, main="Sanofi Non-naive")
corplot(Day15pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_D614G, subset(dat.ocp, naive==0 & company=="Pfz" & Trt==0, c(Day15pseudoneutid50_D614G, Day15pseudoneutid50_BA.4.BA.5)), asp=1, xlim=lim, ylim=lim, main="Pfz,P Non-naive")
Baseline correlation, in the naive
par(mfrow=c(2,2))
lim=c(1.2,5.5)
myboxplot(list(subset(dat.sanofi, naive==1, Bpseudoneutid50_D614G, drop=T),
subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0,Bpseudoneutid50_D614G, drop=T)), test="w", main="D1 Ancestral ID50", ylim=lim, names=c("Sanofi, naive", "Pfz,P, naive"), ylab="")
myboxplot(list(subset(dat.sanofi, naive==1, Bpseudoneutid50_BA.4.BA.5, drop=T),
subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0,Bpseudoneutid50_BA.4.BA.5, drop=T)), test="w", main="D1 BA4BA5 ID50", ylim=lim, names=c("Sanofi, naive", "Pfz,P, naive"), ylab="")
corplot(Bpseudoneutid50_BA.4.BA.5~Bpseudoneutid50_D614G, subset(dat.sanofi, naive==1), asp=1, xlim=lim, ylim=lim, main="Sanofi Naive")
corplot(Bpseudoneutid50_BA.4.BA.5~Bpseudoneutid50_D614G, subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0, c(Bpseudoneutid50_D614G, Bpseudoneutid50_BA.4.BA.5)), asp=1, xlim=lim, ylim=lim, main="Pfz,P Naive")
Baseline correlation, in the non-naive
par(mfrow=c(2,2))
lim=c(1.2,5.5)
myboxplot(list(subset(dat.sanofi, naive==0, Bpseudoneutid50_D614G, drop=T),
subset(dat.ocp, naive==0 & company=="Pfz" & Trt==0,Bpseudoneutid50_D614G, drop=T)), test="w", main="D1 Ancestral ID50", ylim=lim, names=c("Sanofi, Non-naive", "Pfz,P, Non-naive"), ylab="")
myboxplot(list(subset(dat.sanofi, naive==0, Bpseudoneutid50_BA.4.BA.5, drop=T),
subset(dat.ocp, naive==0 & company=="Pfz" & Trt==0,Bpseudoneutid50_BA.4.BA.5, drop=T)), test="w", main="D1 BA4BA5 ID50", ylim=lim, names=c("Sanofi, Non-naive", "Pfz,P, Non-naive"), ylab="")
corplot(Bpseudoneutid50_BA.4.BA.5~Bpseudoneutid50_D614G, subset(dat.sanofi, naive==0), asp=1, xlim=lim, ylim=lim, main="Sanofi Non-naive")
corplot(Bpseudoneutid50_BA.4.BA.5~Bpseudoneutid50_D614G, subset(dat.ocp, naive==0 & company=="Pfz" & Trt==0, c(Bpseudoneutid50_D614G, Bpseudoneutid50_BA.4.BA.5)), asp=1, xlim=lim, ylim=lim, main="Pfz,P Non-naive")
Interaction plot, naive
par(mfrow=c(1,2))
lim=c(1.2,4.8)
# myboxplot(subset(dat.sanofi, naive==1, c(Day15pseudoneutid50_D614G, Day15pseudoneutid50_BA.4.BA.5)), names=c("ancestral", "BA4BA5"), main="Sanofi, naive", add.interaction=T)
# myboxplot(subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0, c(Day15pseudoneutid50_D614G, Day15pseudoneutid50_BA.4.BA.5)), names=c("ancestral", "BA4BA5"), main="Pfz,P, naive", add.interaction=T)Correlation of fold change between ancestral and BA4BA5
dat.sanofi$Day15overBpseudoneutid50_BA.4.BA.5 = dat.sanofi$Day15pseudoneutid50_BA.4.BA.5 - dat.sanofi$Bpseudoneutid50_BA.4.BA.5
dat.sanofi$Day15overBpseudoneutid50_D614G = dat.sanofi$Day15pseudoneutid50_D614G - dat.sanofi$Bpseudoneutid50_D614G
dat.ocp$Day15overBpseudoneutid50_BA.4.BA.5 = dat.ocp$Day15pseudoneutid50_BA.4.BA.5 - dat.ocp$Bpseudoneutid50_BA.4.BA.5
dat.ocp$Day15overBpseudoneutid50_D614G = dat.ocp$Day15pseudoneutid50_D614G - dat.ocp$Bpseudoneutid50_D614G
dat.sanofi$Day29overBpseudoneutid50_BA.4.BA.5 = dat.sanofi$Day29pseudoneutid50_BA.4.BA.5 - dat.sanofi$Bpseudoneutid50_BA.4.BA.5
dat.sanofi$Day29overBpseudoneutid50_D614G = dat.sanofi$Day29pseudoneutid50_D614G - dat.sanofi$Bpseudoneutid50_D614G
dat.ocp$Day29overBpseudoneutid50_BA.4.BA.5 = dat.ocp$Day29pseudoneutid50_BA.4.BA.5 - dat.ocp$Bpseudoneutid50_BA.4.BA.5
dat.ocp$Day29overBpseudoneutid50_D614G = dat.ocp$Day29pseudoneutid50_D614G - dat.ocp$Bpseudoneutid50_D614GCorrelation between D15 over D1 fold change in ancestral and BA4BA5 ID50.
par(mfrow=c(1,3))
lim1=c(0,3.3)
corplot(Day15overBpseudoneutid50_BA.4.BA.5~Day15overBpseudoneutid50_D614G, subset(dat.sanofi, naive==1 & treatment_actual=="Prototype (Sanofi)"), asp=1, xlim=lim1, ylim=lim1, main="Sanofi Prototype Naive")
corplot(Day15overBpseudoneutid50_BA.4.BA.5~Day15overBpseudoneutid50_D614G, subset(dat.sanofi, naive==1 & treatment_actual!="Prototype (Sanofi)" & Day15overBpseudoneutid50_D614G<3), asp=1, xlim=lim1, ylim=lim1, main="Sanofi Beta-containing Naive")
corplot(Day15overBpseudoneutid50_BA.4.BA.5~Day15overBpseudoneutid50_D614G, subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0), asp=1, xlim=lim1, ylim=lim1, main="Pfz,P, naive")
D29 over D1
par(mfrow=c(1,3))
lim1=c(0,3.3)
corplot(Day29overBpseudoneutid50_BA.4.BA.5~Day29overBpseudoneutid50_D614G, subset(dat.sanofi, naive==1 & treatment_actual=="Prototype (Sanofi)"), asp=1, xlim=lim1, ylim=lim1, main="Sanofi Prototype Naive")
corplot(Day29overBpseudoneutid50_BA.4.BA.5~Day29overBpseudoneutid50_D614G, subset(dat.sanofi, naive==1 & treatment_actual!="Prototype (Sanofi)" & Day29overBpseudoneutid50_D614G<3), asp=1, xlim=lim1, ylim=lim1, main="Sanofi Beta-containing Naive")
corplot(Day29overBpseudoneutid50_BA.4.BA.5~Day29overBpseudoneutid50_D614G, subset(dat.ocp, naive==1 & company=="Pfz" & Trt==0), asp=1, xlim=lim1, ylim=lim1, main="Pfz,P, naive")
comparing fold change between different Sanofi arms
par(mfrow=c(1,2))
myboxplot(list(
subset(dat.sanofi, naive==1 & treatment_actual=="Prototype (Sanofi)", Day15overBpseudoneutid50_BA.4.BA.5, drop=T),
subset(dat.sanofi, naive==1 & treatment_actual!="Prototype (Sanofi)", Day15overBpseudoneutid50_BA.4.BA.5, drop=T)), test="w", names=c("Prototype", "Non-prototype"))
myboxplot(list(
subset(dat.sanofi, naive==1 & treatment_actual=="Prototype (Sanofi)", Day15overBpseudoneutid50_D614G, drop=T),
subset(dat.sanofi, naive==1 & treatment_actual!="Prototype (Sanofi)", Day15overBpseudoneutid50_D614G, drop=T)), test="w", names=c("Prototype", "Non-prototype"))
Comparing D29 and D15. Quite comparable
par(mfrow=c(1,2))
lim1=NULL
corplot(Day29pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_BA.4.BA.5, subset(dat.sanofi, naive==1 & treatment_actual=="Prototype (Sanofi)"), asp=1, xlim=lim1, ylim=lim1, main="Sanofi,P Naive")
corplot(Day29pseudoneutid50_BA.4.BA.5~Day15pseudoneutid50_BA.4.BA.5, subset(dat.sanofi, naive==1 & treatment_actual!="Prototype (Sanofi)"), asp=1, xlim=lim1, ylim=lim1, main="Sanofi,NP Naive")
correlation between m15 and baseline markers
# mypairs(dat[,paste0("B",assays)])
# mypairs(dat[,paste0("Day15",assays)])
# mypairs(dat[,paste0(c("B","Day15", "Delta15overB"),assays[1])])marker missingness
table(dat_proc$AsympInfectIndD15to29, dat_proc$ph1.D15)##
## 0 1
## 0 110 1116
## 1 3 11table(dat_proc$arm, dat_proc$ph1.D15)##
## 0 1
## 1 1 96
## 2 3 109
## 3 85 0
## 4 2 98
## 5 3 96
## 6 4 92
## 7 0 47
## 8 1 50
## 9 1 52
## 10 2 49
## 11 2 49
## 12 0 52
## 13 1 45
## 14 2 48
## 15 1 49
## 16 2 98
## 17 3 97table(dat_mapped$ph1.D15, dat_mapped$Immunemarkerset, dat_mapped$arm==3)## , , = FALSE
##
##
## 0 1
## 0 49 0
## 1 0 1127
##
## , , = TRUE
##
##
## 0 1
## 0 1 85
## 1 0 0# across assays, all or none
summary(subset(dat_mapped, ph1.D15==1)["Day15"%.%assays[1:5]])## Day15pseudoneutid50_D614G Day15pseudoneutid50_Delta Day15pseudoneutid50_Beta
## Min. :1.301 Min. :1.301 Min. :1.301
## 1st Qu.:4.039 1st Qu.:3.739 1st Qu.:3.643
## Median :4.348 Median :4.069 Median :4.035
## Mean :4.311 Mean :4.050 Mean :3.999
## 3rd Qu.:4.627 3rd Qu.:4.396 3rd Qu.:4.414
## Max. :5.666 Max. :5.381 Max. :5.592
##
## Day15pseudoneutid50_BA.1 Day15pseudoneutid50_BA.4.BA.5
## Min. :1.301 Min. :1.301
## 1st Qu.:3.238 1st Qu.:2.954
## Median :3.636 Median :3.379
## Mean :3.615 Mean :3.335
## 3rd Qu.:4.050 3rd Qu.:3.786
## Max. :5.225 Max. :5.361
## NA's :666summary(subset(dat_mapped, ph1.D15==1)["Day29"%.%assays[1:5]])## Day29pseudoneutid50_D614G Day29pseudoneutid50_Delta Day29pseudoneutid50_Beta
## Min. :1.301 Min. :1.301 Min. :1.301
## 1st Qu.:3.985 1st Qu.:3.687 1st Qu.:3.544
## Median :4.281 Median :4.002 Median :3.938
## Mean :4.265 Mean :3.993 Mean :3.912
## 3rd Qu.:4.570 3rd Qu.:4.320 3rd Qu.:4.312
## Max. :5.605 Max. :5.402 Max. :5.414
## NA's :15 NA's :15 NA's :15
## Day29pseudoneutid50_BA.1 Day29pseudoneutid50_BA.4.BA.5
## Min. :1.301 Min. :1.301
## 1st Qu.:3.114 1st Qu.:2.889
## Median :3.513 Median :3.272
## Mean :3.482 Mean :3.232
## 3rd Qu.:3.894 3rd Qu.:3.649
## Max. :5.326 Max. :4.907
## NA's :16 NA's :15summary(subset(dat_mapped, ph1.D15==1)["Day91"%.%assays[1:5]])## Day91pseudoneutid50_D614G Day91pseudoneutid50_Delta Day91pseudoneutid50_Beta
## Min. :1.301 Min. :1.301 Min. :1.301
## 1st Qu.:3.768 1st Qu.:3.474 1st Qu.:3.337
## Median :4.115 Median :3.818 Median :3.729
## Mean :4.100 Mean :3.807 Mean :3.724
## 3rd Qu.:4.457 3rd Qu.:4.164 3rd Qu.:4.170
## Max. :5.624 Max. :5.438 Max. :5.420
## NA's :27 NA's :27 NA's :27
## Day91pseudoneutid50_BA.1 Day91pseudoneutid50_BA.4.BA.5
## Min. :1.301 Min. :1.301
## 1st Qu.:2.937 1st Qu.:2.715
## Median :3.396 Median :3.169
## Mean :3.337 Mean :3.121
## 3rd Qu.:3.779 3rd Qu.:3.611
## Max. :5.190 Max. :4.975
## NA's :27 NA's :27summary(subset(dat_mapped, ph1.D15==1)["Day181"%.%assays[1:5]])## Day181pseudoneutid50_D614G Day181pseudoneutid50_Delta
## Min. :1.301 Min. :1.301
## 1st Qu.:3.639 1st Qu.:3.282
## Median :3.980 Median :3.646
## Mean :3.960 Mean :3.617
## 3rd Qu.:4.338 3rd Qu.:3.970
## Max. :5.601 Max. :5.118
## NA's :59 NA's :59
## Day181pseudoneutid50_Beta Day181pseudoneutid50_BA.1
## Min. :1.301 Min. :1.301
## 1st Qu.:3.135 1st Qu.:2.852
## Median :3.602 Median :3.286
## Mean :3.551 Mean :3.229
## 3rd Qu.:3.997 3rd Qu.:3.738
## Max. :5.298 Max. :5.228
## NA's :59 NA's :59
## Day181pseudoneutid50_BA.4.BA.5
## Min. :1.301
## 1st Qu.:2.603
## Median :3.124
## Mean :3.056
## 3rd Qu.:3.591
## Max. :4.913
## NA's :59summary(subset(dat_proc, ph1.D15 & is.na(Day29pseudoneutid50_D614G))["Day29"%.%assays[1:5]])## Day29pseudoneutid50_D614G Day29pseudoneutid50_Delta Day29pseudoneutid50_Beta
## Min. : NA Min. : NA Min. : NA
## 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA
## Median : NA Median : NA Median : NA
## Mean :NaN Mean :NaN Mean :NaN
## 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA
## Max. : NA Max. : NA Max. : NA
## NA's :15 NA's :15 NA's :15
## Day29pseudoneutid50_BA.1 Day29pseudoneutid50_BA.4.BA.5
## Min. : NA Min. : NA
## 1st Qu.: NA 1st Qu.: NA
## Median : NA Median : NA
## Mean :NaN Mean :NaN
## 3rd Qu.: NA 3rd Qu.: NA
## Max. : NA Max. : NA
## NA's :15 NA's :15summary(subset(dat_proc, ph1.D15 & is.na(Day91pseudoneutid50_D614G))["Day91"%.%assays[1:5]])## Day91pseudoneutid50_D614G Day91pseudoneutid50_Delta Day91pseudoneutid50_Beta
## Min. : NA Min. : NA Min. : NA
## 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA
## Median : NA Median : NA Median : NA
## Mean :NaN Mean :NaN Mean :NaN
## 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA
## Max. : NA Max. : NA Max. : NA
## NA's :27 NA's :27 NA's :27
## Day91pseudoneutid50_BA.1 Day91pseudoneutid50_BA.4.BA.5
## Min. : NA Min. : NA
## 1st Qu.: NA 1st Qu.: NA
## Median : NA Median : NA
## Mean :NaN Mean :NaN
## 3rd Qu.: NA 3rd Qu.: NA
## Max. : NA Max. : NA
## NA's :27 NA's :27summary(subset(dat_proc, ph1.D15 & is.na(Day181pseudoneutid50_D614G))["Day181"%.%assays[1:5]])## Day181pseudoneutid50_D614G Day181pseudoneutid50_Delta
## Min. : NA Min. : NA
## 1st Qu.: NA 1st Qu.: NA
## Median : NA Median : NA
## Mean :NaN Mean :NaN
## 3rd Qu.: NA 3rd Qu.: NA
## Max. : NA Max. : NA
## NA's :59 NA's :59
## Day181pseudoneutid50_Beta Day181pseudoneutid50_BA.1
## Min. : NA Min. : NA
## 1st Qu.: NA 1st Qu.: NA
## Median : NA Median : NA
## Mean :NaN Mean :NaN
## 3rd Qu.: NA 3rd Qu.: NA
## Max. : NA Max. : NA
## NA's :59 NA's :59
## Day181pseudoneutid50_BA.4.BA.5
## Min. : NA
## 1st Qu.: NA
## Median : NA
## Mean :NaN
## 3rd Qu.: NA
## Max. : NA
## NA's :59dat_proc$kp = dat_proc$ph1.D15==1 & dat_proc$COVIDIndD22toend!=1 & dat_proc$AsympInfectIndD15to271!=1
dat_proc$kp = dat_proc$ph1.D15==1
for (i in 1:1) {
with(dat_proc[dat_proc$kp,], print(table(!is.na(get("Day29"%.%assays[i])), !is.na(get("Day15"%.%assays[i])))))
with(dat_proc[dat_proc$kp,], print(table(!is.na(get("Day91"%.%assays[i])), !is.na(get("Day15"%.%assays[i])))))
with(dat_proc[dat_proc$kp,], print(table(!is.na(get("Day181"%.%assays[i])), !is.na(get("Day15"%.%assays[i])))))
}##
## TRUE
## FALSE 15
## TRUE 1112
##
## TRUE
## FALSE 27
## TRUE 1100
##
## TRUE
## FALSE 59
## TRUE 1068myboxplot(
list(dat_proc$Day15pseudoneutid50_MDW[dat_proc$treatment_actual=="Omicron + Wildtype/Prototype (Pfizer 1)"],
dat_proc$Day15pseudoneutid50_MDW[dat_proc$treatment_actual=="1 Dose Omicron + Prototype (Moderna)"])
)
myboxplot(
list(dat_proc$Day15pseudoneutid50_D614G[dat_proc$treatment_actual=="Omicron + Wildtype/Prototype (Pfizer 1)"],
dat_proc$Day15pseudoneutid50_D614G[dat_proc$treatment_actual=="1 Dose Omicron + Prototype (Moderna)"])
)
# my.interaction.plot(subset(dat_proc, ph1==1,
# c(Bpseudoneutid50_BA.1, Day15pseudoneutid50_BA.1)),
# x.ori = 0, xaxislabels = c("B", "D15"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
#
# my.interaction.plot(subset(dat_proc, ph1==1,
# c(Day15pseudoneutid50_BA.1, Day29pseudoneutid50_BA.1)),
# x.ori = 1, xaxislabels = c("D15", "D29"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
#
# my.interaction.plot(subset(dat_proc, ph1==1,
# c(Day29pseudoneutid50_BA.1, Day91pseudoneutid50_BA.1)),
# x.ori = 2, xaxislabels = c("D29", "D91"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
#
# my.interaction.plot(subset(dat_proc, ph1==1,
# c(Day91pseudoneutid50_BA.1, Day181pseudoneutid50_BA.1)),
# x.ori = 3, xaxislabels = c("D91", "D181"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
# marker level over time
par(mfrow=c(4,1), mar=c(2,4,1,1))
dat_proc$ph1 = dat_proc$ph1.D15==1 & dat_proc$COVIDIndD22toend!=1 & dat_proc$AsympInfectIndD15to181!=1 & dat_proc$AsympInfectIndD182to271!=1
plot(0,0,type='n', xlim=c(1,5), ylim=c(1,5), xaxt="n", xlab="", ylab="ID50_BA.1")
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1<1.5,
c(Bpseudoneutid50_BA.1, Day15pseudoneutid50_BA.1)),
x.ori = 0, xaxislabels = c("B", "D15"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1<1.5,
c(Day15pseudoneutid50_BA.1, Day29pseudoneutid50_BA.1)),
x.ori = 1, xaxislabels = c("D15", "D29"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1<1.5,
c(Day29pseudoneutid50_BA.1, Day91pseudoneutid50_BA.1)),
x.ori = 2, xaxislabels = c("D29", "D91"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1<1.5,
c(Day91pseudoneutid50_BA.1, Day181pseudoneutid50_BA.1)),
x.ori = 3, xaxislabels = c("D91", "D181"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
plot(0,0,type='n', xlim=c(1,5), ylim=c(1,5), xaxt="n", xlab="", ylab="ID50_BA.1")
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1>1.5,
c(Bpseudoneutid50_BA.1, Day15pseudoneutid50_BA.1)),
x.ori = 0, xaxislabels = c("B", "D15"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1>1.5,
c(Day15pseudoneutid50_BA.1, Day29pseudoneutid50_BA.1)),
x.ori = 1, xaxislabels = c("D15", "D29"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1>1.5,
c(Day29pseudoneutid50_BA.1, Day91pseudoneutid50_BA.1)),
x.ori = 2, xaxislabels = c("D29", "D91"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1>1.5,
c(Day91pseudoneutid50_BA.1, Day181pseudoneutid50_BA.1)),
x.ori = 3, xaxislabels = c("D91", "D181"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
dat_proc$ph1 = dat_proc$ph1.D92 & dat_proc$ph1.D15==1 & dat_proc$COVIDIndD22toend!=1 & dat_proc$AsympInfectIndD15to181!=1 & dat_proc$AsympInfectIndD182to271!=1
plot(0,0,type='n', xlim=c(1,5), ylim=c(1,5), xaxt="n", xlab="", ylab="ID50_BA.1")
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1<1.5,
c(Bpseudoneutid50_BA.1, Day15pseudoneutid50_BA.1)),
x.ori = 0, xaxislabels = c("B", "D15"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1<1.5,
c(Day15pseudoneutid50_BA.1, Day29pseudoneutid50_BA.1)),
x.ori = 1, xaxislabels = c("D15", "D29"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1<1.5,
c(Day29pseudoneutid50_BA.1, Day91pseudoneutid50_BA.1)),
x.ori = 2, xaxislabels = c("D29", "D91"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1<1.5,
c(Day91pseudoneutid50_BA.1, Day181pseudoneutid50_BA.1)),
x.ori = 3, xaxislabels = c("D91", "D181"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
plot(0,0,type='n', xlim=c(1,5), ylim=c(1,5), xaxt="n", xlab="", ylab="ID50_BA.1")
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1>1.5,
c(Bpseudoneutid50_BA.1, Day15pseudoneutid50_BA.1)),
x.ori = 0, xaxislabels = c("B", "D15"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1>1.5,
c(Day15pseudoneutid50_BA.1, Day29pseudoneutid50_BA.1)),
x.ori = 1, xaxislabels = c("D15", "D29"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1>1.5,
c(Day29pseudoneutid50_BA.1, Day91pseudoneutid50_BA.1)),
x.ori = 2, xaxislabels = c("D29", "D91"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL)
my.interaction.plot(subset(dat_proc, ph1==1 & Bpseudoneutid50_BA.1>1.5,
c(Day91pseudoneutid50_BA.1, Day181pseudoneutid50_BA.1)),
x.ori = 3, xaxislabels = c("D91", "D181"), cex.axis = 1, add = T, xlab = "", ylab = "", pcol = NULL, lcol = NULL) 
sum(dat_proc$ph1.D15==1)## [1] 1127dat_proc$kp = dat_proc$ph1.D15==1 & dat_proc$COVIDIndD22toend!=1 & dat_proc$AsympInfectIndD15to271!=1 &
!is.na(dat_proc$Day29pseudoneutid50_D614G) & !is.na(dat_proc$Day91pseudoneutid50_D614G) & !is.na(dat_proc$Day181pseudoneutid50_D614G)
nrow(subset(dat_proc, kp))## [1] 733nrow(subset(dat_proc, kp & Bpseudoneutid50_BA.1>1.5))## [1] 619nrow(subset(dat_proc, kp & Bpseudoneutid50_BA.1<1.5))## [1] 114Misc
myboxplot(dat_mapped[, c("B"%.%assays[1:5], "Day15"%.%assays[1:5])], names=sub("pseudoneutid50_", "", rep(assays[1:5],2)))
summary(dat_mapped)## Subjectid EthnicityHispanic EthnicityNotreported race
## Length:1262 Min. :0.00000 Min. :0.0000000 Length:1262
## Class :character 1st Qu.:0.00000 1st Qu.:0.0000000 Class :character
## Mode :character Median :0.00000 Median :0.0000000 Mode :character
## Mean :0.09113 Mean :0.0007924
## 3rd Qu.:0.00000 3rd Qu.:0.0000000
## Max. :1.00000 Max. :1.0000000
##
## Asian Black NatAmer PacIsl
## Min. :0.0000 Min. :0.00000 Min. :0.00000 Min. :0.000000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.000000
## Median :0.0000 Median :0.00000 Median :0.00000 Median :0.000000
## Mean :0.1173 Mean :0.09033 Mean :0.01426 Mean :0.001585
## 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.000000
## Max. :1.0000 Max. :1.00000 Max. :1.00000 Max. :1.000000
##
## White Multiracial Unknown Age
## Min. :0.0000 Min. :0.00000 Min. :0.000000 Min. :18.00
## 1st Qu.:1.0000 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:33.00
## Median :1.0000 Median :0.00000 Median :0.000000 Median :45.00
## Mean :0.8035 Mean :0.03487 Mean :0.009509 Mean :47.66
## 3rd Qu.:1.0000 3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:65.00
## Max. :1.0000 Max. :1.00000 Max. :1.000000 Max. :85.00
##
## Age65C State City Sex
## Min. :0.0000 Length:1262 Length:1262 Min. :0.0000
## 1st Qu.:0.0000 Class :character Class :character 1st Qu.:0.0000
## Median :0.0000 Mode :character Mode :character Median :1.0000
## Mean :0.2647 Mean :0.5452
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
##
## naive eligibility_deviation early_term_date oos_boost_date
## Min. :0.0000 Length:1262 Length:1262 Length:1262
## 1st Qu.:0.0000 Class :character Class :character Class :character
## Median :1.0000 Mode :character Mode :character Mode :character
## Mean :0.6513
## 3rd Qu.:1.0000
## Max. :1.0000
##
## RESULT RESULT_limit UNITS ASSAY
## Min. : 41.0 Length:1262 Length:1262 Length:1262
## 1st Qu.: 251.5 Class :character Class :character Class :character
## Median : 646.0 Mode :character Mode :character Mode :character
## Mean : 2117.9
## 3rd Qu.: 2093.8
## Max. :48003.0
## NA's :238
## stage treatment_actual treatment_assigned stratalab
## Min. :1.000 Length:1262 Length:1262 Length:1262
## 1st Qu.:1.000 Class :character Class :character Class :character
## Median :2.000 Mode :character Mode :character Mode :character
## Mean :1.966
## 3rd Qu.:3.000
## Max. :4.000
##
## primary_vax1_date primary_vax2_date pre_study_booster_date
## Length:1262 Length:1262 Length:1262
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## primary_booster_type pre_study_infect_date studydose1date
## Length:1262 Length:1262 Length:1262
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## studydose2date early_term oos_boost infect_date1
## Length:1262 Length:1262 Length:1262 Length:1262
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## infect_date2 symptomatic_infect1 symptomatic_infect2 NAntibody
## Length:1262 Length:1262 Length:1262 Length:1262
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## CTSTAT_SWAB1 CTVALUE_SWAB1 ASSSTAT_SWAB1 ASSQC_SWAB1
## Min. :1 Min. :14.00 Min. :1.000 Length:1262
## 1st Qu.:1 1st Qu.:19.70 1st Qu.:1.000 Class :character
## Median :1 Median :22.40 Median :1.000 Mode :character
## Mean :1 Mean :22.80 Mean :1.022
## 3rd Qu.:1 3rd Qu.:25.75 3rd Qu.:1.000
## Max. :1 Max. :32.80 Max. :2.000
## NA's :983 NA's :983 NA's :983
## TOTREADS_SWAB1 COVRDPCT_SWAB1 HSTRDPCT_SWAB1 BACRDPCT_SWAB1
## Min. : 38148 Min. : 5.64 Min. : 0.000 Min. : 0.0000
## 1st Qu.:239706 1st Qu.:95.51 1st Qu.: 0.040 1st Qu.: 0.0600
## Median :317509 Median :97.23 Median : 0.140 Median : 0.1100
## Mean :312744 Mean :95.57 Mean : 1.228 Mean : 0.3963
## 3rd Qu.:383596 3rd Qu.:98.36 3rd Qu.: 0.905 3rd Qu.: 0.3650
## Max. :764242 Max. :99.22 Max. :29.430 Max. :40.7300
## NA's :983 NA's :983 NA's :983 NA's :983
## ASSCOMP_SWAB1 PANGOLIN_SWAB1 PANGOVER_SWAB1 SCORCALL_SWAB1
## Min. :0.7400 Length:1262 Length:1262 Length:1262
## 1st Qu.:0.9900 Class :character Class :character Class :character
## Median :0.9900 Mode :character Mode :character Mode :character
## Mean :0.9834
## 3rd Qu.:0.9900
## Max. :0.9900
## NA's :983
## SCORVER_SWAB1 TRUNCLIN_SWAB1 TRUNCVER_SWAB1 SPKAASUB_SWAB1
## Length:1262 Length:1262 Length:1262 Length:1262
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## SPKAADEL_SWAB1 SPKAAINS_SWAB1 DATE_COLLECT_SWAB1 CTSTAT_SWAB2
## Length:1262 Mode:logical Length:1262 Min. :1
## Class :character NA's:1262 Class :character 1st Qu.:1
## Mode :character Mode :character Median :1
## Mean :1
## 3rd Qu.:1
## Max. :1
## NA's :1261
## CTVALUE_SWAB2 ASSSTAT_SWAB2 ASSQC_SWAB2 TOTREADS_SWAB2
## Min. :24.8 Min. :1 Length:1262 Min. :94834
## 1st Qu.:24.8 1st Qu.:1 Class :character 1st Qu.:94834
## Median :24.8 Median :1 Mode :character Median :94834
## Mean :24.8 Mean :1 Mean :94834
## 3rd Qu.:24.8 3rd Qu.:1 3rd Qu.:94834
## Max. :24.8 Max. :1 Max. :94834
## NA's :1261 NA's :1261 NA's :1261
## COVRDPCT_SWAB2 HSTRDPCT_SWAB2 BACRDPCT_SWAB2 ASSCOMP_SWAB2
## Min. :97.38 Min. :0.71 Min. :0.61 Min. :0.99
## 1st Qu.:97.38 1st Qu.:0.71 1st Qu.:0.61 1st Qu.:0.99
## Median :97.38 Median :0.71 Median :0.61 Median :0.99
## Mean :97.38 Mean :0.71 Mean :0.61 Mean :0.99
## 3rd Qu.:97.38 3rd Qu.:0.71 3rd Qu.:0.61 3rd Qu.:0.99
## Max. :97.38 Max. :0.71 Max. :0.61 Max. :0.99
## NA's :1261 NA's :1261 NA's :1261 NA's :1261
## PANGOLIN_SWAB2 PANGOVER_SWAB2 SCORCALL_SWAB2 SCORVER_SWAB2
## Length:1262 Length:1262 Length:1262 Length:1262
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## TRUNCLIN_SWAB2 TRUNCVER_SWAB2 SPKAASUB_SWAB2 SPKAADEL_SWAB2
## Length:1262 Length:1262 Length:1262 Length:1262
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## SPKAAINS_SWAB2 DATE_COLLECT_SWAB2 TrtmRNA arm
## Mode:logical Length:1262 Min. :0.0000 Min. : 1.000
## NA's:1262 Class :character 1st Qu.:1.0000 1st Qu.: 4.000
## Mode :character Median :1.0000 Median : 7.000
## Mean :0.8796 Mean : 8.315
## 3rd Qu.:1.0000 3rd Qu.:13.000
## Max. :1.0000 Max. :17.000
##
## TrtA TrtB TrtC TrtonedosemRNA
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:1.0000
## Median :1.0000 Median :0.0000 Median :1.0000 Median :1.0000
## Mean :0.6624 Mean :0.1618 Mean :0.8018 Mean :0.8114
## 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :643 NA's :341 NA's :490
## TrtSanofi pre_study_booster_until_studydose1_day
## Min. :0.0000 Min. : 11.0
## 1st Qu.:0.0000 1st Qu.:164.0
## Median :0.0000 Median :191.0
## Mean :0.1204 Mean :207.7
## 3rd Qu.:0.0000 3rd Qu.:230.0
## Max. :1.0000 Max. :592.0
##
## pre_study_booster_until_studydose1_day_median
## Min. :191
## 1st Qu.:191
## Median :191
## Mean :191
## 3rd Qu.:191
## Max. :191
##
## pre_study_booster_until_studydose1_ind D1_D15_flag D1_D29_flag
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:1.0000
## Median :0.0000 Median :1.0000 Median :1.0000
## Mean :0.4984 Mean :0.9818 Mean :0.9802
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## Bpseudoneutid50_BA.1 Bpseudoneutid50_Beta Bpseudoneutid50_Delta
## Min. :1.301 Min. :1.301 Min. :1.301
## 1st Qu.:2.072 1st Qu.:2.590 1st Qu.:2.904
## Median :2.612 Median :3.130 Median :3.357
## Mean :2.583 Mean :3.072 Mean :3.315
## 3rd Qu.:3.181 3rd Qu.:3.606 3rd Qu.:3.775
## Max. :4.681 Max. :4.917 Max. :4.893
## NA's :1 NA's :1 NA's :1
## Bpseudoneutid50Duke_BA.2.12.1 Bpseudoneutid50_BA.4.BA.5 Bpseudoneutid50_D614G
## Min. :1.301 Min. :1.301 Min. :1.301
## 1st Qu.:2.041 1st Qu.:1.301 1st Qu.:3.257
## Median :2.394 Median :2.425 Median :3.649
## Mean :2.396 Mean :2.341 Mean :3.636
## 3rd Qu.:2.833 3rd Qu.:2.990 3rd Qu.:4.096
## Max. :4.235 Max. :4.385 Max. :5.084
## NA's :1137 NA's :1 NA's :1
## Day15pseudoneutid50_BA.1 Day15pseudoneutid50_Beta Day15pseudoneutid50_Delta
## Min. :1.301 Min. :1.301 Min. :1.301
## 1st Qu.:3.239 1st Qu.:3.648 1st Qu.:3.734
## Median :3.631 Median :4.035 Median :4.067
## Mean :3.613 Mean :4.000 Mean :4.044
## 3rd Qu.:4.053 3rd Qu.:4.415 3rd Qu.:4.391
## Max. :5.225 Max. :5.592 Max. :5.381
## NA's :22 NA's :22 NA's :22
## Day15pseudoneutid50Duke_BA.2.12.1 Day15pseudoneutid50_BA.4.BA.5
## Min. :1.301 Min. :1.301
## 1st Qu.:3.078 1st Qu.:2.952
## Median :3.382 Median :3.380
## Mean :3.390 Mean :3.335
## 3rd Qu.:3.695 3rd Qu.:3.784
## Max. :4.859 Max. :5.361
## NA's :1137 NA's :791
## Day15pseudoneutid50_D614G Day29pseudoneutid50_BA.1 Day29pseudoneutid50_Beta
## Min. :1.301 Min. :1.301 Min. :1.301
## 1st Qu.:4.035 1st Qu.:3.119 1st Qu.:3.546
## Median :4.343 Median :3.514 Median :3.931
## Mean :4.307 Mean :3.485 Mean :3.913
## 3rd Qu.:4.627 3rd Qu.:3.898 3rd Qu.:4.324
## Max. :5.666 Max. :5.326 Max. :5.414
## NA's :22 NA's :25 NA's :24
## Day29pseudoneutid50_Delta Day29pseudoneutid50_BA.4.BA.5
## Min. :1.301 Min. :1.301
## 1st Qu.:3.679 1st Qu.:2.889
## Median :3.998 Median :3.267
## Mean :3.988 Mean :3.229
## 3rd Qu.:4.320 3rd Qu.:3.644
## Max. :5.402 Max. :4.907
## NA's :24 NA's :24
## Day29pseudoneutid50_D614G Day91pseudoneutid50_BA.1 Day91pseudoneutid50_Beta
## Min. :1.301 Min. :1.301 Min. :1.301
## 1st Qu.:3.968 1st Qu.:2.948 1st Qu.:3.349
## Median :4.278 Median :3.400 Median :3.739
## Mean :4.256 Mean :3.340 Mean :3.731
## 3rd Qu.:4.574 3rd Qu.:3.779 3rd Qu.:4.179
## Max. :5.605 Max. :5.190 Max. :5.420
## NA's :24 NA's :121 NA's :121
## Day91pseudoneutid50_Delta Day91pseudoneutid50_BA.4.BA.5
## Min. :1.301 Min. :1.301
## 1st Qu.:3.482 1st Qu.:2.719
## Median :3.821 Median :3.187
## Mean :3.815 Mean :3.126
## 3rd Qu.:4.180 3rd Qu.:3.615
## Max. :5.438 Max. :4.975
## NA's :121 NA's :121
## Day91pseudoneutid50_D614G Day181pseudoneutid50_BA.1 Day181pseudoneutid50_Beta
## Min. :1.301 Min. :1.301 Min. :1.301
## 1st Qu.:3.772 1st Qu.:2.855 1st Qu.:3.155
## Median :4.122 Median :3.297 Median :3.614
## Mean :4.107 Mean :3.237 Mean :3.561
## 3rd Qu.:4.459 3rd Qu.:3.749 3rd Qu.:4.021
## Max. :5.624 Max. :5.228 Max. :5.298
## NA's :121 NA's :157 NA's :157
## Day181pseudoneutid50_Delta Day181pseudoneutid50_BA.4.BA.5
## Min. :1.301 Min. :1.301
## 1st Qu.:3.289 1st Qu.:2.612
## Median :3.657 Median :3.154
## Mean :3.623 Mean :3.065
## 3rd Qu.:3.991 3rd Qu.:3.599
## Max. :5.118 Max. :4.913
## NA's :157 NA's :157
## Day181pseudoneutid50_D614G Day85pseudoneutid50_BA.1 Day85pseudoneutid50_Beta
## Min. :1.301 Min. :1.301 Min. :1.301
## 1st Qu.:3.656 1st Qu.:3.289 1st Qu.:3.646
## Median :3.993 Median :3.574 Median :4.078
## Mean :3.966 Mean :3.614 Mean :4.012
## 3rd Qu.:4.346 3rd Qu.:4.009 3rd Qu.:4.365
## Max. :5.601 Max. :4.837 Max. :5.144
## NA's :157 NA's :1177 NA's :1177
## Day85pseudoneutid50_Delta Day85pseudoneutid50_BA.4.BA.5
## Min. :1.301 Min. :1.301
## 1st Qu.:3.710 1st Qu.:2.982
## Median :3.942 Median :3.215
## Mean :3.994 Mean :3.307
## 3rd Qu.:4.365 3rd Qu.:3.709
## Max. :5.082 Max. :4.623
## NA's :1177 NA's :1177
## Day85pseudoneutid50_D614G Day147pseudoneutid50_BA.1 Day147pseudoneutid50_Beta
## Min. :1.301 Min. :1.301 Min. :1.301
## 1st Qu.:3.892 1st Qu.:3.030 1st Qu.:3.255
## Median :4.189 Median :3.384 Median :3.721
## Mean :4.213 Mean :3.418 Mean :3.704
## 3rd Qu.:4.557 3rd Qu.:3.886 3rd Qu.:4.092
## Max. :5.179 Max. :4.796 Max. :4.740
## NA's :1177 NA's :1176 NA's :1176
## Day147pseudoneutid50_Delta Day147pseudoneutid50_BA.4.BA.5
## Min. :1.301 Min. :1.301
## 1st Qu.:3.369 1st Qu.:2.616
## Median :3.694 Median :3.070
## Mean :3.700 Mean :3.078
## 3rd Qu.:4.130 3rd Qu.:3.696
## Max. :4.803 Max. :4.481
## NA's :1176 NA's :1176
## Day147pseudoneutid50_D614G symptm_infect1 symptm_infect1_date
## Min. :1.301 Min. :0.0000 Length:1262
## 1st Qu.:3.630 1st Qu.:0.0000 Class :character
## Median :3.984 Median :0.0000 Mode :character
## Mean :3.999 Mean :0.2979
## 3rd Qu.:4.373 3rd Qu.:1.0000
## Max. :4.963 Max. :1.0000
## NA's :1176
## N_status_1.0 N_status_15.2 N_status_181.14 N_status_271.14
## Min. :0.0000 Min. :0 Min. :0.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0 1st Qu.:0.000 1st Qu.:0.0000
## Median :0.0000 Median :0 Median :1.000 Median :1.0000
## Mean :0.3315 Mean :0 Mean :0.552 Mean :0.5795
## 3rd Qu.:1.0000 3rd Qu.:0 3rd Qu.:1.000 3rd Qu.:1.0000
## Max. :1.0000 Max. :0 Max. :1.000 Max. :1.0000
## NA's :1 NA's :22 NA's :157 NA's :375
## N_status_29.2 N_status_91.7 Actual_visit_date_1.0 Actual_visit_date_15.2
## Min. :0.0000 Min. :0.0000 Length:1262 Length:1262
## 1st Qu.:0.0000 1st Qu.:0.0000 Class :character Class :character
## Median :0.0000 Median :0.0000 Mode :character Mode :character
## Mean :0.3586 Mean :0.4663
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :24 NA's :121
## Actual_visit_date_181.14 Actual_visit_date_271.14 Actual_visit_date_29.2
## Length:1262 Length:1262 Length:1262
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## Actual_visit_date_91.7 first_Npos_date Target_study_day_numeric
## Length:1262 Length:1262 Min. : 1.00
## Class :character Class :character 1st Qu.: 1.00
## Mode :character Mode :character Median : 1.00
## Mean : 71.81
## 3rd Qu.:181.00
## Max. :271.00
## NA's :497
## NumberdaysD15toD29 NumberdaysD15toD91 NumberdaysD15toD181 last_contact_date
## Min. : 6.00 Min. : 54.00 Min. : 98.0 Length:1262
## 1st Qu.:13.00 1st Qu.: 70.00 1st Qu.:154.0 Class :character
## Median :14.00 Median : 75.00 Median :159.0 Mode :character
## Mean :14.18 Mean : 74.64 Mean :158.9
## 3rd Qu.:15.00 3rd Qu.: 77.00 3rd Qu.:167.0
## Max. :34.00 Max. :102.00 Max. :191.0
## NA's :38 NA's :134 NA's :173
## Perprotocol symptm_infect1_date_imp oos_boost_date_imp
## Min. :0.0000 Length:1262 Length:1262
## 1st Qu.:1.0000 Class :character Class :character
## Median :1.0000 Mode :character Mode :character
## Mean :0.9794
## 3rd Qu.:1.0000
## Max. :1.0000
##
## early_term_date_imp EarlyendpointD15 Immunemarkerset
## Length:1262 Min. :0.00000 Min. :0.0000
## Class :character 1st Qu.:0.00000 1st Qu.:1.0000
## Mode :character Median :0.00000 Median :1.0000
## Mean :0.01936 Mean :0.9604
## 3rd Qu.:0.00000 3rd Qu.:1.0000
## Max. :1.00000 Max. :1.0000
## NA's :22
## ImmunemarkersetD92toD181 ph1.D15 ph1.D92 ph1.D29
## Min. :0.0000 Min. :0.000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:1.000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :1.000 Median :1.0000 Median :0.0000
## Mean :0.7971 Mean :0.893 Mean :0.7393 Mean :0.1086
## 3rd Qu.:1.0000 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.000 Max. :1.0000 Max. :1.0000
##
## CNSR1_D22toD91 CNSR1_D92toD181 CNSR1_D22toD181 CNSR1_D36toD181
## Length:1262 Length:1262 Length:1262 Length:1262
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## CNSR1_D22toend CNSR2 CNSR3 CNSR4
## Length:1262 Length:1262 Length:1262 Length:1262
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## CNSR5 CNSR_D22toD91 CNSR_D92toD181 CNSR_D22toD181
## Length:1262 Length:1262 Length:1262 Length:1262
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## CNSR_D36toD181 CNSR_D22toend COVIDtimeD22toD91 COVIDIndD22toD91
## Length:1262 Length:1262 Min. :-14.00 Min. :0.0000
## Class :character Class :character 1st Qu.: 91.00 1st Qu.:0.0000
## Mode :character Mode :character Median : 91.00 Median :0.0000
## Mean : 79.48 Mean :0.1332
## 3rd Qu.: 91.00 3rd Qu.:0.0000
## Max. : 91.00 Max. :1.0000
## NA's :22 NA's :46
## COVIDtimeD92toD181 COVIDIndD92toD181 COVIDtimeD22toD181 COVIDIndD22toD181
## Min. :-14.0 Min. :0.00000 Min. :-14.0 Min. :0.0000
## 1st Qu.: 94.0 1st Qu.:0.00000 1st Qu.: 94.0 1st Qu.:0.0000
## Median :171.0 Median :0.00000 Median :171.0 Median :0.0000
## Mean :140.2 Mean :0.07102 Mean :140.2 Mean :0.1941
## 3rd Qu.:188.0 3rd Qu.:0.00000 3rd Qu.:188.0 3rd Qu.:0.0000
## Max. :188.0 Max. :1.00000 Max. :188.0 Max. :1.0000
## NA's :22 NA's :220 NA's :22 NA's :46
## COVIDtimeD36toD181 COVIDIndD36toD181 COVIDIndD22toend AsympInfectIndD15to29
## Min. :-27.0 Min. :0.000 Min. :0.0000 Min. :0.00000
## 1st Qu.: 82.0 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :156.0 Median :0.000 Median :0.0000 Median :0.00000
## Mean :132.3 Mean :0.179 Mean :0.2163 Mean :0.01109
## 3rd Qu.:188.0 3rd Qu.:0.000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :188.0 Max. :1.000 Max. :1.0000 Max. :1.00000
## NA's :24 NA's :72 NA's :46
## AsympInfectIndD30to91 AsympInfectIndD15to91 AsympInfectIndD92to181
## Min. :0.00000 Min. :0.00000 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.0000
## Median :0.00000 Median :0.00000 Median :0.0000
## Mean :0.01664 Mean :0.02773 Mean :0.0206
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.0000
## Max. :1.00000 Max. :1.00000 Max. :1.0000
##
## AsympInfectIndD182to271 AsympInfectIndD15to181 AsympInfectIndD15to271
## Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.01347 Mean :0.04834 Mean :0.06181
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000
##
## Bcd4_154_BA.4.5.S1 Bcd4_154_BA.4.5.S2 Bcd4_154_COV2.CON.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0277 1st Qu.:0.0390 1st Qu.:0.0434
## Median :0.0531 Median :0.0677 Median :0.0688
## Mean :0.0753 Mean :0.0905 Mean :0.1005
## 3rd Qu.:0.0946 3rd Qu.:0.1155 3rd Qu.:0.1159
## Max. :0.6903 Max. :0.5670 Max. :0.7643
## NA's :692 NA's :710 NA's :686
## Bcd4_154_COV2.CON.S2 Bcd4_154_Wuhan.N Bcd4_CXCR5.154_BA.4.5.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0439 1st Qu.:0.0010 1st Qu.:0.0011
## Median :0.0796 Median :0.0069 Median :0.0031
## Mean :0.1026 Mean :0.0165 Mean :0.0044
## 3rd Qu.:0.1308 3rd Qu.:0.0178 3rd Qu.:0.0059
## Max. :0.5934 Max. :0.4365 Max. :0.0339
## NA's :686 NA's :745 NA's :700
## Bcd4_CXCR5.154_BA.4.5.S2 Bcd4_CXCR5.154_COV2.CON.S1 Bcd4_CXCR5.154_COV2.CON.S2
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0012 1st Qu.:0.0022 1st Qu.:0.0018
## Median :0.0032 Median :0.0046 Median :0.0039
## Mean :0.0045 Mean :0.0065 Mean :0.0054
## 3rd Qu.:0.0058 3rd Qu.:0.0086 3rd Qu.:0.0070
## Max. :0.0866 Max. :0.0477 Max. :0.1106
## NA's :713 NA's :687 NA's :686
## Bcd4_CXCR5.154_Wuhan.N Bcd4_CXCR5.IL21_BA.4.5.S1 Bcd4_CXCR5.IL21_BA.4.5.S2
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010 Median :0.0010
## Mean :0.0026 Mean :0.0017 Mean :0.0018
## 3rd Qu.:0.0027 3rd Qu.:0.0019 3rd Qu.:0.0020
## Max. :0.0522 Max. :0.0135 Max. :0.0139
## NA's :750 NA's :700 NA's :713
## Bcd4_CXCR5.IL21_COV2.CON.S1 Bcd4_CXCR5.IL21_COV2.CON.S2
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0011 Median :0.0010
## Mean :0.0023 Mean :0.0019
## 3rd Qu.:0.0027 3rd Qu.:0.0020
## Max. :0.0174 Max. :0.0164
## NA's :687 NA's :686
## Bcd4_CXCR5.IL21_Wuhan.N Bcd4_IFNg_BA.4.5.S1 Bcd4_IFNg_BA.4.5.S2
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0164 1st Qu.:0.0267
## Median :0.0010 Median :0.0334 Median :0.0466
## Mean :0.0014 Mean :0.0485 Mean :0.0620
## 3rd Qu.:0.0010 3rd Qu.:0.0575 3rd Qu.:0.0818
## Max. :0.0395 Max. :0.7835 Max. :0.5082
## NA's :750 NA's :692 NA's :710
## Bcd4_IFNg_COV2.CON.S1 Bcd4_IFNg_COV2.CON.S2 Bcd4_IFNg_Wuhan.N
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0251 1st Qu.:0.0320 1st Qu.:0.0010
## Median :0.0446 Median :0.0556 Median :0.0035
## Mean :0.0634 Mean :0.0708 Mean :0.0107
## 3rd Qu.:0.0748 3rd Qu.:0.0891 3rd Qu.:0.0108
## Max. :0.7517 Max. :0.5097 Max. :0.2599
## NA's :686 NA's :686 NA's :745
## Bcd4_IFNg.IL2_BA.4.5.S1 Bcd4_IFNg.IL2_BA.4.5.S2 Bcd4_IFNg.IL2_COV2.CON.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0275 1st Qu.:0.0388 1st Qu.:0.0396
## Median :0.0525 Median :0.0728 Median :0.0710
## Mean :0.0798 Mean :0.0996 Mean :0.1054
## 3rd Qu.:0.0931 3rd Qu.:0.1277 3rd Qu.:0.1247
## Max. :0.8216 Max. :0.7870 Max. :0.8138
## NA's :692 NA's :710 NA's :686
## Bcd4_IFNg.IL2_COV2.CON.S2 Bcd4_IFNg.IL2_Wuhan.N Bcd4_IFNg.IL2.154_BA.4.5.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0478 1st Qu.:0.0010 1st Qu.:0.0317
## Median :0.0848 Median :0.0038 Median :0.0604
## Mean :0.1136 Mean :0.0158 Mean :0.0896
## 3rd Qu.:0.1458 3rd Qu.:0.0176 3rd Qu.:0.1094
## Max. :0.7656 Max. :0.3488 Max. :0.8375
## NA's :686 NA's :745 NA's :692
## Bcd4_IFNg.IL2.154_BA.4.5.S2 Bcd4_IFNg.IL2.154_COV2.CON.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0451 1st Qu.:0.0471
## Median :0.0817 Median :0.0803
## Mean :0.1103 Mean :0.1185
## 3rd Qu.:0.1399 3rd Qu.:0.1404
## Max. :0.8358 Max. :0.8391
## NA's :710 NA's :686
## Bcd4_IFNg.IL2.154_COV2.CON.S2 Bcd4_IFNg.IL2.154_Wuhan.N Bcd4_IL17a_BA.4.5.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0534 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0951 Median :0.0048 Median :0.0010
## Mean :0.1256 Mean :0.0181 Mean :0.0016
## 3rd Qu.:0.1624 3rd Qu.:0.0200 3rd Qu.:0.0014
## Max. :0.8387 Max. :0.4696 Max. :0.0115
## NA's :686 NA's :745 NA's :692
## Bcd4_IL17a_BA.4.5.S2 Bcd4_IL17a_COV2.CON.S1 Bcd4_IL17a_COV2.CON.S2
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010 Median :0.0010
## Mean :0.0015 Mean :0.0015 Mean :0.0016
## 3rd Qu.:0.0011 3rd Qu.:0.0013 3rd Qu.:0.0013
## Max. :0.0104 Max. :0.0114 Max. :0.0115
## NA's :710 NA's :686 NA's :686
## Bcd4_IL17a_Wuhan.N Bcd4_IL2_BA.4.5.S1 Bcd4_IL2_BA.4.5.S2 Bcd4_IL2_COV2.CON.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0202 1st Qu.:0.0298 1st Qu.:0.0294
## Median :0.0010 Median :0.0431 Median :0.0571 Median :0.0569
## Mean :0.0013 Mean :0.0672 Mean :0.0830 Mean :0.0894
## 3rd Qu.:0.0010 3rd Qu.:0.0781 3rd Qu.:0.1068 3rd Qu.:0.1022
## Max. :0.0103 Max. :0.6065 Max. :0.7696 Max. :0.7052
## NA's :745 NA's :692 NA's :710 NA's :686
## Bcd4_IL2_COV2.CON.S2 Bcd4_IL2_Wuhan.N Bcd4_IL21_BA.4.5.S1 Bcd4_IL21_BA.4.5.S2
## Min. :0.0010 Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0347 1st Qu.:0.0010 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0654 Median :0.0031 Median :0.0013 Median :0.0027
## Mean :0.0948 Mean :0.0132 Mean :0.0039 Mean :0.0051
## 3rd Qu.:0.1198 3rd Qu.:0.0147 3rd Qu.:0.0047 3rd Qu.:0.0065
## Max. :0.7439 Max. :0.3340 Max. :0.0441 Max. :0.0679
## NA's :686 NA's :745 NA's :692 NA's :710
## Bcd4_IL21_COV2.CON.S1 Bcd4_IL21_COV2.CON.S2 Bcd4_IL21_Wuhan.N
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0030 Median :0.0029 Median :0.0010
## Mean :0.0057 Mean :0.0057 Mean :0.0033
## 3rd Qu.:0.0073 3rd Qu.:0.0073 3rd Qu.:0.0030
## Max. :0.0802 Max. :0.0612 Max. :0.1515
## NA's :686 NA's :686 NA's :745
## Bcd4_IL4.154_BA.4.5.S1 Bcd4_IL4.154_BA.4.5.S2 Bcd4_IL4.154_COV2.CON.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010 Median :0.0010
## Mean :0.0016 Mean :0.0016 Mean :0.0018
## 3rd Qu.:0.0010 3rd Qu.:0.0010 3rd Qu.:0.0013
## Max. :0.0294 Max. :0.0250 Max. :0.0276
## NA's :692 NA's :710 NA's :686
## Bcd4_IL4.154_COV2.CON.S2 Bcd4_IL4.154_Wuhan.N Bcd4_IL4.IL5.IL13.154_BA.4.5.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010 Median :0.0010
## Mean :0.0017 Mean :0.0010 Mean :0.0019
## 3rd Qu.:0.0012 3rd Qu.:0.0010 3rd Qu.:0.0013
## Max. :0.0441 Max. :0.0075 Max. :0.0330
## NA's :686 NA's :745 NA's :692
## Bcd4_IL4.IL5.IL13.154_BA.4.5.S2 Bcd4_IL4.IL5.IL13.154_COV2.CON.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010
## Mean :0.0019 Mean :0.0022
## 3rd Qu.:0.0013 3rd Qu.:0.0016
## Max. :0.0375 Max. :0.0329
## NA's :710 NA's :686
## Bcd4_IL4.IL5.IL13.154_COV2.CON.S2 Bcd4_IL4.IL5.IL13.154_Wuhan.N
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010
## Mean :0.0020 Mean :0.0011
## 3rd Qu.:0.0014 3rd Qu.:0.0010
## Max. :0.0573 Max. :0.0083
## NA's :686 NA's :745
## Bcd4_IL5.IL13.154_BA.4.5.S1 Bcd4_IL5.IL13.154_BA.4.5.S2
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010
## Mean :0.0015 Mean :0.0014
## 3rd Qu.:0.0010 3rd Qu.:0.0010
## Max. :0.0214 Max. :0.0236
## NA's :692 NA's :710
## Bcd4_IL5.IL13.154_COV2.CON.S1 Bcd4_IL5.IL13.154_COV2.CON.S2
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010
## Mean :0.0017 Mean :0.0015
## 3rd Qu.:0.0010 3rd Qu.:0.0010
## Max. :0.0302 Max. :0.0338
## NA's :686 NA's :686
## Bcd4_IL5.IL13.154_Wuhan.N Bcd4_TNFa_BA.4.5.S1 Bcd4_TNFa_BA.4.5.S2
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0040 1st Qu.:0.0140
## Median :0.0010 Median :0.0409 Median :0.0514
## Mean :0.0010 Mean :0.0709 Mean :0.0865
## 3rd Qu.:0.0010 3rd Qu.:0.0879 3rd Qu.:0.1175
## Max. :0.0039 Max. :0.8326 Max. :0.9873
## NA's :745 NA's :692 NA's :710
## Bcd4_TNFa_COV2.CON.S1 Bcd4_TNFa_COV2.CON.S2 Bcd4_TNFa_Wuhan.N
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0061 1st Qu.:0.0151 1st Qu.:0.0010
## Median :0.0536 Median :0.0620 Median :0.0010
## Mean :0.0883 Mean :0.0990 Mean :0.0101
## 3rd Qu.:0.1105 3rd Qu.:0.1420 3rd Qu.:0.0010
## Max. :0.9469 Max. :0.9527 Max. :0.5128
## NA's :686 NA's :686 NA's :745
## Bcd8_IFNg_BA.4.5.S1 Bcd8_IFNg_BA.4.5.S2 Bcd8_IFNg_COV2.CON.S1
## Min. :0.0010 Min. :0.0010 Min. : 0.0010
## 1st Qu.:0.0061 1st Qu.:0.0018 1st Qu.: 0.0062
## Median :0.0323 Median :0.0097 Median : 0.0351
## Mean :0.2588 Mean :0.0538 Mean : 0.2730
## 3rd Qu.:0.1269 3rd Qu.:0.0355 3rd Qu.: 0.1464
## Max. :8.9190 Max. :2.5966 Max. :10.2187
## NA's :694 NA's :710 NA's :686
## Bcd8_IFNg_COV2.CON.S2 Bcd8_IFNg_Wuhan.N Bcd8_IFNg.IL2_BA.4.5.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0011 1st Qu.:0.0010 1st Qu.:0.0064
## Median :0.0104 Median :0.0010 Median :0.0343
## Mean :0.0560 Mean :0.0126 Mean :0.2605
## 3rd Qu.:0.0319 3rd Qu.:0.0053 3rd Qu.:0.1280
## Max. :4.9023 Max. :0.9490 Max. :8.9239
## NA's :686 NA's :746 NA's :694
## Bcd8_IFNg.IL2_BA.4.5.S2 Bcd8_IFNg.IL2_COV2.CON.S1 Bcd8_IFNg.IL2_COV2.CON.S2
## Min. :0.0010 Min. : 0.0010 Min. :0.0010
## 1st Qu.:0.0011 1st Qu.: 0.0065 1st Qu.:0.0011
## Median :0.0102 Median : 0.0360 Median :0.0109
## Mean :0.0538 Mean : 0.2749 Mean :0.0563
## 3rd Qu.:0.0357 3rd Qu.: 0.1461 3rd Qu.:0.0330
## Max. :2.5966 Max. :10.2283 Max. :4.9113
## NA's :710 NA's :686 NA's :686
## Bcd8_IFNg.IL2_Wuhan.N Bcd8_IFNg.IL2.TNFa_BA.4.5.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0075
## Median :0.0010 Median :0.0442
## Mean :0.0128 Mean :0.2722
## 3rd Qu.:0.0062 3rd Qu.:0.1436
## Max. :0.9464 Max. :9.6164
## NA's :746 NA's :694
## Bcd8_IFNg.IL2.TNFa_BA.4.5.S2 Bcd8_IFNg.IL2.TNFa_COV2.CON.S1
## Min. :0.0010 Min. : 0.0010
## 1st Qu.:0.0010 1st Qu.: 0.0067
## Median :0.0123 Median : 0.0374
## Mean :0.0615 Mean : 0.2861
## 3rd Qu.:0.0447 3rd Qu.: 0.1563
## Max. :2.6188 Max. :10.7786
## NA's :710 NA's :686
## Bcd8_IFNg.IL2.TNFa_COV2.CON.S2 Bcd8_IFNg.IL2.TNFa_Wuhan.N Bcd8_IL2_BA.4.5.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0131 Median :0.0010 Median :0.0065
## Mean :0.0611 Mean :0.0146 Mean :0.0319
## 3rd Qu.:0.0393 3rd Qu.:0.0102 3rd Qu.:0.0249
## Max. :5.1185 Max. :0.9392 Max. :1.0542
## NA's :686 NA's :746 NA's :694
## Bcd8_IL2_BA.4.5.S2 Bcd8_IL2_COV2.CON.S1 Bcd8_IL2_COV2.CON.S2 Bcd8_IL2_Wuhan.N
## Min. :0.0010 Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0017 Median :0.0070 Median :0.0021 Median :0.0010
## Mean :0.0075 Mean :0.0296 Mean :0.0084 Mean :0.0051
## 3rd Qu.:0.0062 3rd Qu.:0.0245 3rd Qu.:0.0063 3rd Qu.:0.0026
## Max. :0.2089 Max. :0.6245 Max. :0.2640 Max. :0.3005
## NA's :710 NA's :686 NA's :686 NA's :746
## Bcd8_TNFa_BA.4.5.S1 Bcd8_TNFa_BA.4.5.S2 Bcd8_TNFa_COV2.CON.S1
## Min. :0.0010 Min. :0.0010 Min. : 0.0010
## 1st Qu.:0.0048 1st Qu.:0.0010 1st Qu.: 0.0031
## Median :0.0304 Median :0.0083 Median : 0.0257
## Mean :0.2273 Mean :0.0478 Mean : 0.2277
## 3rd Qu.:0.1071 3rd Qu.:0.0318 3rd Qu.: 0.1144
## Max. :9.0860 Max. :1.8457 Max. :10.0111
## NA's :694 NA's :710 NA's :686
## Bcd8_TNFa_COV2.CON.S2 Bcd8_TNFa_Wuhan.N Day15cd4_154_BA.4.5.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010 1st Qu.:0.0547
## Median :0.0074 Median :0.0010 Median :0.0879
## Mean :0.0483 Mean :0.0103 Mean :0.1260
## 3rd Qu.:0.0257 3rd Qu.:0.0080 3rd Qu.:0.1577
## Max. :4.8425 Max. :0.8312 Max. :1.3949
## NA's :686 NA's :746 NA's :693
## Day15cd4_154_BA.4.5.S2 Day15cd4_154_COV2.CON.S1 Day15cd4_154_COV2.CON.S2
## Min. :0.0010 Min. :0.0079 Min. :0.0041
## 1st Qu.:0.0673 1st Qu.:0.0746 1st Qu.:0.0742
## Median :0.1124 Median :0.1185 Median :0.1290
## Mean :0.1488 Mean :0.1627 Mean :0.1622
## 3rd Qu.:0.1977 3rd Qu.:0.1953 3rd Qu.:0.2037
## Max. :0.8099 Max. :1.6033 Max. :1.0294
## NA's :703 NA's :686 NA's :686
## Day15cd4_154_Wuhan.N Day15cd4_CXCR5.154_BA.4.5.S1 Day15cd4_CXCR5.154_BA.4.5.S2
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0018 1st Qu.:0.0023 1st Qu.:0.0028
## Median :0.0081 Median :0.0054 Median :0.0053
## Mean :0.0176 Mean :0.0088 Mean :0.0090
## 3rd Qu.:0.0204 3rd Qu.:0.0107 3rd Qu.:0.0102
## Max. :0.4795 Max. :0.1835 Max. :0.4291
## NA's :717 NA's :701 NA's :706
## Day15cd4_CXCR5.154_COV2.CON.S1 Day15cd4_CXCR5.154_COV2.CON.S2
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0045 1st Qu.:0.0033
## Median :0.0089 Median :0.0072
## Mean :0.0132 Mean :0.0108
## 3rd Qu.:0.0169 3rd Qu.:0.0123
## Max. :0.2213 Max. :0.4744
## NA's :687 NA's :686
## Day15cd4_CXCR5.154_Wuhan.N Day15cd4_CXCR5.IL21_BA.4.5.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0011
## Mean :0.0026 Mean :0.0023
## 3rd Qu.:0.0024 3rd Qu.:0.0026
## Max. :0.0636 Max. :0.0265
## NA's :723 NA's :701
## Day15cd4_CXCR5.IL21_BA.4.5.S2 Day15cd4_CXCR5.IL21_COV2.CON.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0012 Median :0.0020
## Mean :0.0023 Mean :0.0036
## 3rd Qu.:0.0030 3rd Qu.:0.0047
## Max. :0.0150 Max. :0.0321
## NA's :706 NA's :687
## Day15cd4_CXCR5.IL21_COV2.CON.S2 Day15cd4_CXCR5.IL21_Wuhan.N
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0015 Median :0.0010
## Mean :0.0028 Mean :0.0013
## 3rd Qu.:0.0035 3rd Qu.:0.0010
## Max. :0.0221 Max. :0.0217
## NA's :686 NA's :723
## Day15cd4_IFNg_BA.4.5.S1 Day15cd4_IFNg_BA.4.5.S2 Day15cd4_IFNg_COV2.CON.S1
## Min. :0.0010 Min. :0.0032 Min. :0.0010
## 1st Qu.:0.0378 1st Qu.:0.0518 1st Qu.:0.0529
## Median :0.0666 Median :0.0901 Median :0.0883
## Mean :0.0927 Mean :0.1182 Mean :0.1199
## 3rd Qu.:0.1126 3rd Qu.:0.1552 3rd Qu.:0.1436
## Max. :1.0689 Max. :0.7476 Max. :1.1782
## NA's :693 NA's :703 NA's :686
## Day15cd4_IFNg_COV2.CON.S2 Day15cd4_IFNg_Wuhan.N Day15cd4_IFNg.IL2_BA.4.5.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0579 1st Qu.:0.0010 1st Qu.:0.0556
## Median :0.1000 Median :0.0044 Median :0.0941
## Mean :0.1277 Mean :0.0117 Mean :0.1315
## 3rd Qu.:0.1692 3rd Qu.:0.0131 3rd Qu.:0.1643
## Max. :0.8221 Max. :0.2068 Max. :1.2915
## NA's :686 NA's :717 NA's :693
## Day15cd4_IFNg.IL2_BA.4.5.S2 Day15cd4_IFNg.IL2_COV2.CON.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0723 1st Qu.:0.0778
## Median :0.1218 Median :0.1233
## Mean :0.1610 Mean :0.1700
## 3rd Qu.:0.2120 3rd Qu.:0.2029
## Max. :0.7926 Max. :1.4200
## NA's :703 NA's :686
## Day15cd4_IFNg.IL2_COV2.CON.S2 Day15cd4_IFNg.IL2_Wuhan.N
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0832 1st Qu.:0.0010
## Median :0.1386 Median :0.0055
## Mean :0.1760 Mean :0.0162
## 3rd Qu.:0.2258 3rd Qu.:0.0190
## Max. :0.9630 Max. :0.4074
## NA's :686 NA's :717
## Day15cd4_IFNg.IL2.154_BA.4.5.S1 Day15cd4_IFNg.IL2.154_BA.4.5.S2
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0637 1st Qu.:0.0803
## Median :0.1046 Median :0.1395
## Mean :0.1483 Mean :0.1778
## 3rd Qu.:0.1883 3rd Qu.:0.2287
## Max. :1.4881 Max. :0.9375
## NA's :693 NA's :703
## Day15cd4_IFNg.IL2.154_COV2.CON.S1 Day15cd4_IFNg.IL2.154_COV2.CON.S2
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0861 1st Qu.:0.0905
## Median :0.1416 Median :0.1524
## Mean :0.1907 Mean :0.1952
## 3rd Qu.:0.2285 3rd Qu.:0.2491
## Max. :1.6908 Max. :1.1756
## NA's :686 NA's :686
## Day15cd4_IFNg.IL2.154_Wuhan.N Day15cd4_IL17a_BA.4.5.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0067 Median :0.0010
## Mean :0.0187 Mean :0.0017
## 3rd Qu.:0.0224 3rd Qu.:0.0014
## Max. :0.5227 Max. :0.0139
## NA's :717 NA's :693
## Day15cd4_IL17a_BA.4.5.S2 Day15cd4_IL17a_COV2.CON.S1 Day15cd4_IL17a_COV2.CON.S2
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010 Median :0.0010
## Mean :0.0017 Mean :0.0019 Mean :0.0018
## 3rd Qu.:0.0017 3rd Qu.:0.0017 3rd Qu.:0.0017
## Max. :0.0152 Max. :0.0224 Max. :0.0176
## NA's :703 NA's :686 NA's :686
## Day15cd4_IL17a_Wuhan.N Day15cd4_IL2_BA.4.5.S1 Day15cd4_IL2_BA.4.5.S2
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0335 1st Qu.:0.0415
## Median :0.0010 Median :0.0584 Median :0.0749
## Mean :0.0013 Mean :0.0894 Mean :0.1057
## 3rd Qu.:0.0010 3rd Qu.:0.1137 3rd Qu.:0.1363
## Max. :0.0109 Max. :0.8232 Max. :0.6468
## NA's :717 NA's :693 NA's :703
## Day15cd4_IL2_COV2.CON.S1 Day15cd4_IL2_COV2.CON.S2 Day15cd4_IL2_Wuhan.N
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0450 1st Qu.:0.0497 1st Qu.:0.0010
## Median :0.0804 Median :0.0858 Median :0.0037
## Mean :0.1162 Mean :0.1180 Mean :0.0128
## 3rd Qu.:0.1366 3rd Qu.:0.1491 3rd Qu.:0.0150
## Max. :0.9056 Max. :0.7909 Max. :0.3852
## NA's :686 NA's :686 NA's :717
## Day15cd4_IL21_BA.4.5.S1 Day15cd4_IL21_BA.4.5.S2 Day15cd4_IL21_COV2.CON.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0047 1st Qu.:0.0071 1st Qu.:0.0078
## Median :0.0126 Median :0.0159 Median :0.0171
## Mean :0.0177 Mean :0.0226 Mean :0.0238
## 3rd Qu.:0.0243 3rd Qu.:0.0300 3rd Qu.:0.0315
## Max. :0.2645 Max. :0.2539 Max. :0.2822
## NA's :693 NA's :703 NA's :686
## Day15cd4_IL21_COV2.CON.S2 Day15cd4_IL21_Wuhan.N Day15cd4_IL4.154_BA.4.5.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0077 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0177 Median :0.0010 Median :0.0010
## Mean :0.0250 Mean :0.0034 Mean :0.0028
## 3rd Qu.:0.0331 3rd Qu.:0.0039 3rd Qu.:0.0026
## Max. :0.2899 Max. :0.0630 Max. :0.0353
## NA's :686 NA's :717 NA's :693
## Day15cd4_IL4.154_BA.4.5.S2 Day15cd4_IL4.154_COV2.CON.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0011 Median :0.0013
## Mean :0.0029 Mean :0.0037
## 3rd Qu.:0.0028 3rd Qu.:0.0035
## Max. :0.0530 Max. :0.0607
## NA's :703 NA's :686
## Day15cd4_IL4.154_COV2.CON.S2 Day15cd4_IL4.154_Wuhan.N
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0013 Median :0.0010
## Mean :0.0033 Mean :0.0011
## 3rd Qu.:0.0033 3rd Qu.:0.0010
## Max. :0.0569 Max. :0.0073
## NA's :686 NA's :717
## Day15cd4_IL4.IL5.IL13.154_BA.4.5.S1 Day15cd4_IL4.IL5.IL13.154_BA.4.5.S2
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0011 Median :0.0013
## Mean :0.0033 Mean :0.0035
## 3rd Qu.:0.0032 3rd Qu.:0.0034
## Max. :0.0522 Max. :0.0608
## NA's :693 NA's :703
## Day15cd4_IL4.IL5.IL13.154_COV2.CON.S1 Day15cd4_IL4.IL5.IL13.154_COV2.CON.S2
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0015 Median :0.0015
## Mean :0.0046 Mean :0.0039
## 3rd Qu.:0.0045 3rd Qu.:0.0042
## Max. :0.0767 Max. :0.0599
## NA's :686 NA's :686
## Day15cd4_IL4.IL5.IL13.154_Wuhan.N Day15cd4_IL5.IL13.154_BA.4.5.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010
## Mean :0.0011 Mean :0.0020
## 3rd Qu.:0.0010 3rd Qu.:0.0012
## Max. :0.0106 Max. :0.0364
## NA's :717 NA's :693
## Day15cd4_IL5.IL13.154_BA.4.5.S2 Day15cd4_IL5.IL13.154_COV2.CON.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010
## Mean :0.0020 Mean :0.0026
## 3rd Qu.:0.0014 3rd Qu.:0.0017
## Max. :0.0412 Max. :0.0479
## NA's :703 NA's :686
## Day15cd4_IL5.IL13.154_COV2.CON.S2 Day15cd4_IL5.IL13.154_Wuhan.N
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010
## Mean :0.0020 Mean :0.0011
## 3rd Qu.:0.0015 3rd Qu.:0.0010
## Max. :0.0358 Max. :0.0065
## NA's :686 NA's :717
## Day15cd4_TNFa_BA.4.5.S1 Day15cd4_TNFa_BA.4.5.S2 Day15cd4_TNFa_COV2.CON.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0303 1st Qu.:0.0441 1st Qu.:0.0330
## Median :0.0734 Median :0.0960 Median :0.0914
## Mean :0.1168 Mean :0.1385 Mean :0.1402
## 3rd Qu.:0.1558 3rd Qu.:0.1863 3rd Qu.:0.1764
## Max. :1.2295 Max. :0.9793 Max. :1.3111
## NA's :693 NA's :703 NA's :686
## Day15cd4_TNFa_COV2.CON.S2 Day15cd4_TNFa_Wuhan.N Day15cd8_IFNg_BA.4.5.S1
## Min. :0.0010 Min. :0.0010 Min. : 0.0010
## 1st Qu.:0.0458 1st Qu.:0.0010 1st Qu.: 0.0124
## Median :0.1059 Median :0.0010 Median : 0.0487
## Mean :0.1522 Mean :0.0093 Mean : 0.5142
## 3rd Qu.:0.2051 3rd Qu.:0.0061 3rd Qu.: 0.2704
## Max. :1.2638 Max. :0.5546 Max. :16.5486
## NA's :686 NA's :717 NA's :695
## Day15cd8_IFNg_BA.4.5.S2 Day15cd8_IFNg_COV2.CON.S1 Day15cd8_IFNg_COV2.CON.S2
## Min. :0.0010 Min. : 0.0010 Min. :0.0010
## 1st Qu.:0.0029 1st Qu.: 0.0151 1st Qu.:0.0048
## Median :0.0171 Median : 0.0688 Median :0.0156
## Mean :0.1062 Mean : 0.5285 Mean :0.1205
## 3rd Qu.:0.0625 3rd Qu.: 0.3013 3rd Qu.:0.0510
## Max. :9.2387 Max. :16.0068 Max. :8.7407
## NA's :703 NA's :687 NA's :686
## Day15cd8_IFNg_Wuhan.N Day15cd8_IFNg.IL2_BA.4.5.S1 Day15cd8_IFNg.IL2_BA.4.5.S2
## Min. :0.0010 Min. : 0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.: 0.0119 1st Qu.:0.0030
## Median :0.0010 Median : 0.0495 Median :0.0166
## Mean :0.0118 Mean : 0.5150 Mean :0.1063
## 3rd Qu.:0.0066 3rd Qu.: 0.2752 3rd Qu.:0.0627
## Max. :1.0833 Max. :16.5524 Max. :9.2387
## NA's :719 NA's :695 NA's :703
## Day15cd8_IFNg.IL2_COV2.CON.S1 Day15cd8_IFNg.IL2_COV2.CON.S2
## Min. : 0.0010 Min. :0.0010
## 1st Qu.: 0.0159 1st Qu.:0.0041
## Median : 0.0692 Median :0.0149
## Mean : 0.5296 Mean :0.1207
## 3rd Qu.: 0.3030 3rd Qu.:0.0521
## Max. :16.0073 Max. :8.7407
## NA's :687 NA's :686
## Day15cd8_IFNg.IL2_Wuhan.N Day15cd8_IFNg.IL2.TNFa_BA.4.5.S1
## Min. :0.0010 Min. : 0.0010
## 1st Qu.:0.0010 1st Qu.: 0.0132
## Median :0.0010 Median : 0.0594
## Mean :0.0119 Mean : 0.5256
## 3rd Qu.:0.0069 3rd Qu.: 0.2867
## Max. :1.0833 Max. :16.5758
## NA's :719 NA's :695
## Day15cd8_IFNg.IL2.TNFa_BA.4.5.S2 Day15cd8_IFNg.IL2.TNFa_COV2.CON.S1
## Min. :0.0010 Min. : 0.0010
## 1st Qu.:0.0010 1st Qu.: 0.0140
## Median :0.0193 Median : 0.0682
## Mean :0.1134 Mean : 0.5381
## 3rd Qu.:0.0709 3rd Qu.: 0.3210
## Max. :9.2822 Max. :16.0581
## NA's :703 NA's :687
## Day15cd8_IFNg.IL2.TNFa_COV2.CON.S2 Day15cd8_IFNg.IL2.TNFa_Wuhan.N
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0019 1st Qu.:0.0010
## Median :0.0184 Median :0.0010
## Mean :0.1254 Mean :0.0132
## 3rd Qu.:0.0638 3rd Qu.:0.0088
## Max. :8.9171 Max. :1.0908
## NA's :686 NA's :719
## Day15cd8_IL2_BA.4.5.S1 Day15cd8_IL2_BA.4.5.S2 Day15cd8_IL2_COV2.CON.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0063 Median :0.0020 Median :0.0076
## Mean :0.0398 Mean :0.0088 Mean :0.0356
## 3rd Qu.:0.0302 3rd Qu.:0.0065 3rd Qu.:0.0282
## Max. :2.2593 Max. :0.2397 Max. :2.2919
## NA's :695 NA's :703 NA's :687
## Day15cd8_IL2_COV2.CON.S2 Day15cd8_IL2_Wuhan.N Day15cd8_TNFa_BA.4.5.S1
## Min. :0.0010 Min. :0.0010 Min. : 0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010 1st Qu.: 0.0063
## Median :0.0025 Median :0.0010 Median : 0.0319
## Mean :0.0104 Mean :0.0043 Mean : 0.3178
## 3rd Qu.:0.0076 3rd Qu.:0.0025 3rd Qu.: 0.1581
## Max. :0.4038 Max. :0.3199 Max. :15.0926
## NA's :686 NA's :719 NA's :695
## Day15cd8_TNFa_BA.4.5.S2 Day15cd8_TNFa_COV2.CON.S1 Day15cd8_TNFa_COV2.CON.S2
## Min. :0.0010 Min. : 0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.: 0.0045 1st Qu.:0.0010
## Median :0.0107 Median : 0.0300 Median :0.0085
## Mean :0.0640 Mean : 0.2965 Mean :0.0725
## 3rd Qu.:0.0409 3rd Qu.: 0.1721 3rd Qu.:0.0380
## Max. :2.0666 Max. :14.7401 Max. :7.4199
## NA's :703 NA's :687 NA's :686
## Day15cd8_TNFa_Wuhan.N Day91cd4_154_BA.4.5.S1 Day91cd4_154_BA.4.5.S2
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0256 1st Qu.:0.0361
## Median :0.0010 Median :0.0472 Median :0.0635
## Mean :0.0087 Mean :0.0660 Mean :0.0787
## 3rd Qu.:0.0064 3rd Qu.:0.0854 3rd Qu.:0.1006
## Max. :0.9568 Max. :0.3996 Max. :0.4515
## NA's :719 NA's :1092 NA's :1094
## Day91cd4_154_COV2.CON.S1 Day91cd4_154_COV2.CON.S2 Day91cd4_154_Wuhan.N
## Min. :0.0042 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0407 1st Qu.:0.0418 1st Qu.:0.0038
## Median :0.0659 Median :0.0703 Median :0.0118
## Mean :0.0873 Mean :0.0886 Mean :0.0179
## 3rd Qu.:0.1056 3rd Qu.:0.1099 3rd Qu.:0.0260
## Max. :0.5735 Max. :0.6552 Max. :0.1041
## NA's :1088 NA's :1088 NA's :1105
## Day91cd4_CXCR5.154_BA.4.5.S1 Day91cd4_CXCR5.154_BA.4.5.S2
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0012 1st Qu.:0.0010
## Median :0.0031 Median :0.0021
## Mean :0.0045 Mean :0.0037
## 3rd Qu.:0.0061 3rd Qu.:0.0050
## Max. :0.0265 Max. :0.0279
## NA's :1099 NA's :1096
## Day91cd4_CXCR5.154_COV2.CON.S1 Day91cd4_CXCR5.154_COV2.CON.S2
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0017 1st Qu.:0.0010
## Median :0.0036 Median :0.0031
## Mean :0.0053 Mean :0.0044
## 3rd Qu.:0.0066 3rd Qu.:0.0059
## Max. :0.0404 Max. :0.0345
## NA's :1088 NA's :1088
## Day91cd4_CXCR5.154_Wuhan.N Day91cd4_CXCR5.IL21_BA.4.5.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0013 Median :0.0010
## Mean :0.0032 Mean :0.0019
## 3rd Qu.:0.0041 3rd Qu.:0.0024
## Max. :0.0195 Max. :0.0097
## NA's :1111 NA's :1099
## Day91cd4_CXCR5.IL21_BA.4.5.S2 Day91cd4_CXCR5.IL21_COV2.CON.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0012
## Mean :0.0018 Mean :0.0021
## 3rd Qu.:0.0020 3rd Qu.:0.0026
## Max. :0.0143 Max. :0.0112
## NA's :1096 NA's :1088
## Day91cd4_CXCR5.IL21_COV2.CON.S2 Day91cd4_CXCR5.IL21_Wuhan.N
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010
## Mean :0.0020 Mean :0.0013
## 3rd Qu.:0.0023 3rd Qu.:0.0013
## Max. :0.0093 Max. :0.0042
## NA's :1088 NA's :1111
## Day91cd4_IFNg_BA.4.5.S1 Day91cd4_IFNg_BA.4.5.S2 Day91cd4_IFNg_COV2.CON.S1
## Min. :0.0010 Min. :0.0030 Min. :0.0010
## 1st Qu.:0.0221 1st Qu.:0.0303 1st Qu.:0.0314
## Median :0.0377 Median :0.0527 Median :0.0507
## Mean :0.0490 Mean :0.0641 Mean :0.0629
## 3rd Qu.:0.0657 3rd Qu.:0.0823 3rd Qu.:0.0794
## Max. :0.2286 Max. :0.3369 Max. :0.3156
## NA's :1092 NA's :1094 NA's :1088
## Day91cd4_IFNg_COV2.CON.S2 Day91cd4_IFNg_Wuhan.N Day91cd4_IFNg.IL2_BA.4.5.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0370 1st Qu.:0.0011 1st Qu.:0.0320
## Median :0.0614 Median :0.0076 Median :0.0602
## Mean :0.0706 Mean :0.0137 Mean :0.0817
## 3rd Qu.:0.0933 3rd Qu.:0.0195 3rd Qu.:0.0918
## Max. :0.3189 Max. :0.0954 Max. :0.5515
## NA's :1088 NA's :1105 NA's :1092
## Day91cd4_IFNg.IL2_BA.4.5.S2 Day91cd4_IFNg.IL2_COV2.CON.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0442 1st Qu.:0.0456
## Median :0.0702 Median :0.0775
## Mean :0.0979 Mean :0.1031
## 3rd Qu.:0.1218 3rd Qu.:0.1192
## Max. :0.7309 Max. :0.7888
## NA's :1094 NA's :1088
## Day91cd4_IFNg.IL2_COV2.CON.S2 Day91cd4_IFNg.IL2_Wuhan.N
## Min. :0.0019 Min. :0.0010
## 1st Qu.:0.0501 1st Qu.:0.0010
## Median :0.0853 Median :0.0114
## Mean :0.1100 Mean :0.0208
## 3rd Qu.:0.1368 3rd Qu.:0.0285
## Max. :0.7924 Max. :0.1231
## NA's :1088 NA's :1105
## Day91cd4_IFNg.IL2.154_BA.4.5.S1 Day91cd4_IFNg.IL2.154_BA.4.5.S2
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0353 1st Qu.:0.0455
## Median :0.0662 Median :0.0801
## Mean :0.0887 Mean :0.1048
## 3rd Qu.:0.1017 3rd Qu.:0.1291
## Max. :0.5840 Max. :0.7868
## NA's :1092 NA's :1094
## Day91cd4_IFNg.IL2.154_COV2.CON.S1 Day91cd4_IFNg.IL2.154_COV2.CON.S2
## Min. :0.0010 Min. :0.0027
## 1st Qu.:0.0480 1st Qu.:0.0518
## Median :0.0863 Median :0.0919
## Mean :0.1128 Mean :0.1185
## 3rd Qu.:0.1330 3rd Qu.:0.1440
## Max. :0.8670 Max. :0.8962
## NA's :1088 NA's :1088
## Day91cd4_IFNg.IL2.154_Wuhan.N Day91cd4_IL17a_BA.4.5.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0014 1st Qu.:0.0010
## Median :0.0146 Median :0.0010
## Mean :0.0226 Mean :0.0014
## 3rd Qu.:0.0309 3rd Qu.:0.0011
## Max. :0.1292 Max. :0.0101
## NA's :1105 NA's :1092
## Day91cd4_IL17a_BA.4.5.S2 Day91cd4_IL17a_COV2.CON.S1 Day91cd4_IL17a_COV2.CON.S2
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010 Median :0.0010
## Mean :0.0014 Mean :0.0014 Mean :0.0014
## 3rd Qu.:0.0012 3rd Qu.:0.0011 3rd Qu.:0.0012
## Max. :0.0082 Max. :0.0082 Max. :0.0076
## NA's :1094 NA's :1088 NA's :1088
## Day91cd4_IL17a_Wuhan.N Day91cd4_IL2_BA.4.5.S1 Day91cd4_IL2_BA.4.5.S2
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0207 1st Qu.:0.0285
## Median :0.0010 Median :0.0431 Median :0.0492
## Mean :0.0012 Mean :0.0657 Mean :0.0745
## 3rd Qu.:0.0010 3rd Qu.:0.0724 3rd Qu.:0.0918
## Max. :0.0052 Max. :0.5320 Max. :0.6896
## NA's :1105 NA's :1092 NA's :1094
## Day91cd4_IL2_COV2.CON.S1 Day91cd4_IL2_COV2.CON.S2 Day91cd4_IL2_Wuhan.N
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0285 1st Qu.:0.0327 1st Qu.:0.0015
## Median :0.0587 Median :0.0571 Median :0.0091
## Mean :0.0828 Mean :0.0850 Mean :0.0172
## 3rd Qu.:0.0944 3rd Qu.:0.1048 3rd Qu.:0.0227
## Max. :0.7441 Max. :0.7521 Max. :0.1147
## NA's :1088 NA's :1088 NA's :1105
## Day91cd4_IL21_BA.4.5.S1 Day91cd4_IL21_BA.4.5.S2 Day91cd4_IL21_COV2.CON.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0034 Median :0.0038 Median :0.0048
## Mean :0.0063 Mean :0.0065 Mean :0.0073
## 3rd Qu.:0.0089 3rd Qu.:0.0095 3rd Qu.:0.0100
## Max. :0.0446 Max. :0.0579 Max. :0.0534
## NA's :1092 NA's :1094 NA's :1088
## Day91cd4_IL21_COV2.CON.S2 Day91cd4_IL21_Wuhan.N Day91cd4_IL4.154_BA.4.5.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0035 Median :0.0010 Median :0.0010
## Mean :0.0070 Mean :0.0030 Mean :0.0017
## 3rd Qu.:0.0097 3rd Qu.:0.0039 3rd Qu.:0.0010
## Max. :0.0529 Max. :0.0176 Max. :0.0357
## NA's :1088 NA's :1105 NA's :1092
## Day91cd4_IL4.154_BA.4.5.S2 Day91cd4_IL4.154_COV2.CON.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010
## Mean :0.0016 Mean :0.0018
## 3rd Qu.:0.0011 3rd Qu.:0.0012
## Max. :0.0169 Max. :0.0270
## NA's :1094 NA's :1088
## Day91cd4_IL4.154_COV2.CON.S2 Day91cd4_IL4.154_Wuhan.N
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010
## Mean :0.0017 Mean :0.0010
## 3rd Qu.:0.0014 3rd Qu.:0.0010
## Max. :0.0184 Max. :0.0025
## NA's :1088 NA's :1105
## Day91cd4_IL4.IL5.IL13.154_BA.4.5.S1 Day91cd4_IL4.IL5.IL13.154_BA.4.5.S2
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010
## Mean :0.0020 Mean :0.0018
## 3rd Qu.:0.0012 3rd Qu.:0.0014
## Max. :0.0475 Max. :0.0219
## NA's :1092 NA's :1094
## Day91cd4_IL4.IL5.IL13.154_COV2.CON.S1 Day91cd4_IL4.IL5.IL13.154_COV2.CON.S2
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010
## Mean :0.0023 Mean :0.0020
## 3rd Qu.:0.0013 3rd Qu.:0.0016
## Max. :0.0369 Max. :0.0284
## NA's :1088 NA's :1088
## Day91cd4_IL4.IL5.IL13.154_Wuhan.N Day91cd4_IL5.IL13.154_BA.4.5.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010
## Mean :0.0011 Mean :0.0016
## 3rd Qu.:0.0010 3rd Qu.:0.0010
## Max. :0.0033 Max. :0.0329
## NA's :1105 NA's :1092
## Day91cd4_IL5.IL13.154_BA.4.5.S2 Day91cd4_IL5.IL13.154_COV2.CON.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010
## Mean :0.0013 Mean :0.0017
## 3rd Qu.:0.0010 3rd Qu.:0.0010
## Max. :0.0152 Max. :0.0227
## NA's :1094 NA's :1088
## Day91cd4_IL5.IL13.154_COV2.CON.S2 Day91cd4_IL5.IL13.154_Wuhan.N
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010
## Mean :0.0015 Mean :0.0011
## 3rd Qu.:0.0010 3rd Qu.:0.0010
## Max. :0.0184 Max. :0.0033
## NA's :1088 NA's :1105
## Day91cd4_TNFa_BA.4.5.S1 Day91cd4_TNFa_BA.4.5.S2 Day91cd4_TNFa_COV2.CON.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0222 1st Qu.:0.0260 1st Qu.:0.0165
## Median :0.0479 Median :0.0648 Median :0.0605
## Mean :0.0764 Mean :0.0907 Mean :0.0894
## 3rd Qu.:0.0852 3rd Qu.:0.1089 3rd Qu.:0.1055
## Max. :0.6854 Max. :0.9192 Max. :0.9451
## NA's :1092 NA's :1094 NA's :1088
## Day91cd4_TNFa_COV2.CON.S2 Day91cd4_TNFa_Wuhan.N Day91cd8_IFNg_BA.4.5.S1
## Min. :0.0010 Min. :0.0010 Min. : 0.0010
## 1st Qu.:0.0252 1st Qu.:0.0010 1st Qu.: 0.0083
## Median :0.0664 Median :0.0010 Median : 0.0605
## Mean :0.0994 Mean :0.0100 Mean : 0.5581
## 3rd Qu.:0.1222 3rd Qu.:0.0116 3rd Qu.: 0.2957
## Max. :0.9957 Max. :0.1281 Max. :14.2081
## NA's :1088 NA's :1105 NA's :1093
## Day91cd8_IFNg_BA.4.5.S2 Day91cd8_IFNg_COV2.CON.S1 Day91cd8_IFNg_COV2.CON.S2
## Min. :0.0010 Min. : 0.0010 Min. :0.0010
## 1st Qu.:0.0027 1st Qu.: 0.0124 1st Qu.:0.0035
## Median :0.0114 Median : 0.0749 Median :0.0138
## Mean :0.0726 Mean : 0.5225 Mean :0.0780
## 3rd Qu.:0.0510 3rd Qu.: 0.3535 3rd Qu.:0.0437
## Max. :1.6579 Max. :10.6086 Max. :2.2747
## NA's :1094 NA's :1088 NA's :1088
## Day91cd8_IFNg_Wuhan.N Day91cd8_IFNg.IL2_BA.4.5.S1 Day91cd8_IFNg.IL2_BA.4.5.S2
## Min. :0.0010 Min. : 0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.: 0.0096 1st Qu.:0.0023
## Median :0.0016 Median : 0.0605 Median :0.0119
## Mean :0.0110 Mean : 0.5601 Mean :0.0728
## 3rd Qu.:0.0107 3rd Qu.: 0.3005 3rd Qu.:0.0509
## Max. :0.1907 Max. :14.2104 Max. :1.6582
## NA's :1105 NA's :1093 NA's :1094
## Day91cd8_IFNg.IL2_COV2.CON.S1 Day91cd8_IFNg.IL2_COV2.CON.S2
## Min. : 0.0010 Min. :0.0010
## 1st Qu.: 0.0136 1st Qu.:0.0027
## Median : 0.0749 Median :0.0146
## Mean : 0.5241 Mean :0.0783
## 3rd Qu.: 0.3610 3rd Qu.:0.0439
## Max. :10.6086 Max. :2.2722
## NA's :1088 NA's :1088
## Day91cd8_IFNg.IL2_Wuhan.N Day91cd8_IFNg.IL2.TNFa_BA.4.5.S1
## Min. :0.0010 Min. : 0.0010
## 1st Qu.:0.0010 1st Qu.: 0.0196
## Median :0.0025 Median : 0.0759
## Mean :0.0112 Mean : 0.5779
## 3rd Qu.:0.0122 3rd Qu.: 0.3197
## Max. :0.2010 Max. :14.3705
## NA's :1105 NA's :1093
## Day91cd8_IFNg.IL2.TNFa_BA.4.5.S2 Day91cd8_IFNg.IL2.TNFa_COV2.CON.S1
## Min. :0.0010 Min. : 0.0010
## 1st Qu.:0.0039 1st Qu.: 0.0184
## Median :0.0162 Median : 0.0876
## Mean :0.0801 Mean : 0.5433
## 3rd Qu.:0.0534 3rd Qu.: 0.3683
## Max. :1.6615 Max. :10.9200
## NA's :1094 NA's :1088
## Day91cd8_IFNg.IL2.TNFa_COV2.CON.S2 Day91cd8_IFNg.IL2.TNFa_Wuhan.N
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0057 1st Qu.:0.0010
## Median :0.0190 Median :0.0019
## Mean :0.0828 Mean :0.0126
## 3rd Qu.:0.0503 3rd Qu.:0.0142
## Max. :2.2698 Max. :0.1967
## NA's :1088 NA's :1105
## Day91cd8_IL2_BA.4.5.S1 Day91cd8_IL2_BA.4.5.S2 Day91cd8_IL2_COV2.CON.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010 1st Qu.:0.0028
## Median :0.0094 Median :0.0025 Median :0.0099
## Mean :0.0448 Mean :0.0069 Mean :0.0347
## 3rd Qu.:0.0351 3rd Qu.:0.0073 3rd Qu.:0.0332
## Max. :0.9965 Max. :0.1063 Max. :0.4415
## NA's :1093 NA's :1094 NA's :1088
## Day91cd8_IL2_COV2.CON.S2 Day91cd8_IL2_Wuhan.N Day91cd8_TNFa_BA.4.5.S1
## Min. :0.0010 Min. :0.0010 Min. : 0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010 1st Qu.: 0.0142
## Median :0.0028 Median :0.0010 Median : 0.0528
## Mean :0.0073 Mean :0.0046 Mean : 0.4522
## 3rd Qu.:0.0080 3rd Qu.:0.0036 3rd Qu.: 0.2355
## Max. :0.0913 Max. :0.0954 Max. :11.7346
## NA's :1088 NA's :1105 NA's :1093
## Day91cd8_TNFa_BA.4.5.S2 Day91cd8_TNFa_COV2.CON.S1 Day91cd8_TNFa_COV2.CON.S2
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0079 1st Qu.:0.0010
## Median :0.0112 Median :0.0527 Median :0.0122
## Mean :0.0588 Mean :0.3995 Mean :0.0610
## 3rd Qu.:0.0378 3rd Qu.:0.2374 3rd Qu.:0.0364
## Max. :1.0431 Max. :7.2473 Max. :1.6538
## NA's :1094 NA's :1088 NA's :1088
## Day91cd8_TNFa_Wuhan.N Day181cd4_154_BA.4.5.S1 Day181cd4_154_BA.4.5.S2
## Min. :0.0010 Min. :0.0010 Min. :0.0066
## 1st Qu.:0.0010 1st Qu.:0.0266 1st Qu.:0.0356
## Median :0.0010 Median :0.0466 Median :0.0542
## Mean :0.0068 Mean :0.0674 Mean :0.0780
## 3rd Qu.:0.0056 3rd Qu.:0.0786 3rd Qu.:0.0855
## Max. :0.1183 Max. :0.3538 Max. :0.4304
## NA's :1105 NA's :1128 NA's :1131
## Day181cd4_154_COV2.CON.S1 Day181cd4_154_COV2.CON.S2 Day181cd4_154_Wuhan.N
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0363 1st Qu.:0.0382 1st Qu.:0.0038
## Median :0.0574 Median :0.0629 Median :0.0089
## Mean :0.0860 Mean :0.0895 Mean :0.0172
## 3rd Qu.:0.0970 3rd Qu.:0.1096 3rd Qu.:0.0215
## Max. :0.4956 Max. :0.5473 Max. :0.0934
## NA's :1123 NA's :1123 NA's :1137
## Day181cd4_CXCR5.154_BA.4.5.S1 Day181cd4_CXCR5.154_BA.4.5.S2
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0025 Median :0.0018
## Mean :0.0034 Mean :0.0031
## 3rd Qu.:0.0048 3rd Qu.:0.0043
## Max. :0.0174 Max. :0.0154
## NA's :1128 NA's :1131
## Day181cd4_CXCR5.154_COV2.CON.S1 Day181cd4_CXCR5.154_COV2.CON.S2
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0015 1st Qu.:0.0010
## Median :0.0034 Median :0.0023
## Mean :0.0043 Mean :0.0035
## 3rd Qu.:0.0054 3rd Qu.:0.0053
## Max. :0.0173 Max. :0.0203
## NA's :1123 NA's :1123
## Day181cd4_CXCR5.154_Wuhan.N Day181cd4_CXCR5.IL21_BA.4.5.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0013 Median :0.0010
## Mean :0.0023 Mean :0.0020
## 3rd Qu.:0.0026 3rd Qu.:0.0023
## Max. :0.0136 Max. :0.0081
## NA's :1143 NA's :1128
## Day181cd4_CXCR5.IL21_BA.4.5.S2 Day181cd4_CXCR5.IL21_COV2.CON.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0011
## Mean :0.0017 Mean :0.0023
## 3rd Qu.:0.0018 3rd Qu.:0.0032
## Max. :0.0075 Max. :0.0130
## NA's :1131 NA's :1123
## Day181cd4_CXCR5.IL21_COV2.CON.S2 Day181cd4_CXCR5.IL21_Wuhan.N
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010
## Mean :0.0018 Mean :0.0013
## 3rd Qu.:0.0021 3rd Qu.:0.0012
## Max. :0.0090 Max. :0.0060
## NA's :1123 NA's :1143
## Day181cd4_IFNg_BA.4.5.S1 Day181cd4_IFNg_BA.4.5.S2 Day181cd4_IFNg_COV2.CON.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0212 1st Qu.:0.0282 1st Qu.:0.0300
## Median :0.0347 Median :0.0491 Median :0.0481
## Mean :0.0509 Mean :0.0649 Mean :0.0643
## 3rd Qu.:0.0651 3rd Qu.:0.0802 3rd Qu.:0.0795
## Max. :0.2589 Max. :0.3036 Max. :0.3141
## NA's :1128 NA's :1131 NA's :1123
## Day181cd4_IFNg_COV2.CON.S2 Day181cd4_IFNg_Wuhan.N Day181cd4_IFNg.IL2_BA.4.5.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0328 1st Qu.:0.0010 1st Qu.:0.0264
## Median :0.0553 Median :0.0064 Median :0.0540
## Mean :0.0715 Mean :0.0152 Mean :0.0861
## 3rd Qu.:0.0877 3rd Qu.:0.0178 3rd Qu.:0.1103
## Max. :0.3479 Max. :0.1464 Max. :0.5222
## NA's :1123 NA's :1137 NA's :1128
## Day181cd4_IFNg.IL2_BA.4.5.S2 Day181cd4_IFNg.IL2_COV2.CON.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0419 1st Qu.:0.0424
## Median :0.0761 Median :0.0721
## Mean :0.0966 Mean :0.1043
## 3rd Qu.:0.1184 3rd Qu.:0.1115
## Max. :0.5481 Max. :0.6034
## NA's :1131 NA's :1123
## Day181cd4_IFNg.IL2_COV2.CON.S2 Day181cd4_IFNg.IL2_Wuhan.N
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0447 1st Qu.:0.0010
## Median :0.0838 Median :0.0059
## Mean :0.1105 Mean :0.0213
## 3rd Qu.:0.1349 3rd Qu.:0.0290
## Max. :0.6278 Max. :0.1394
## NA's :1123 NA's :1137
## Day181cd4_IFNg.IL2.154_BA.4.5.S1 Day181cd4_IFNg.IL2.154_BA.4.5.S2
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0291 1st Qu.:0.0431
## Median :0.0598 Median :0.0779
## Mean :0.0926 Mean :0.1034
## 3rd Qu.:0.1147 3rd Qu.:0.1250
## Max. :0.5409 Max. :0.6045
## NA's :1128 NA's :1131
## Day181cd4_IFNg.IL2.154_COV2.CON.S1 Day181cd4_IFNg.IL2.154_COV2.CON.S2
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0436 1st Qu.:0.0467
## Median :0.0786 Median :0.0901
## Mean :0.1126 Mean :0.1180
## 3rd Qu.:0.1240 3rd Qu.:0.1480
## Max. :0.6680 Max. :0.7136
## NA's :1123 NA's :1123
## Day181cd4_IFNg.IL2.154_Wuhan.N Day181cd4_IL17a_BA.4.5.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0081 Median :0.0010
## Mean :0.0224 Mean :0.0013
## 3rd Qu.:0.0284 3rd Qu.:0.0010
## Max. :0.1441 Max. :0.0077
## NA's :1137 NA's :1128
## Day181cd4_IL17a_BA.4.5.S2 Day181cd4_IL17a_COV2.CON.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010
## Mean :0.0014 Mean :0.0013
## 3rd Qu.:0.0010 3rd Qu.:0.0010
## Max. :0.0071 Max. :0.0071
## NA's :1131 NA's :1123
## Day181cd4_IL17a_COV2.CON.S2 Day181cd4_IL17a_Wuhan.N Day181cd4_IL2_BA.4.5.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010 1st Qu.:0.0172
## Median :0.0010 Median :0.0010 Median :0.0405
## Mean :0.0013 Mean :0.0013 Mean :0.0680
## 3rd Qu.:0.0010 3rd Qu.:0.0010 3rd Qu.:0.0705
## Max. :0.0078 Max. :0.0055 Max. :0.5035
## NA's :1123 NA's :1137 NA's :1128
## Day181cd4_IL2_BA.4.5.S2 Day181cd4_IL2_COV2.CON.S1 Day181cd4_IL2_COV2.CON.S2
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0295 1st Qu.:0.0269 1st Qu.:0.0292
## Median :0.0521 Median :0.0508 Median :0.0596
## Mean :0.0744 Mean :0.0819 Mean :0.0853
## 3rd Qu.:0.0923 3rd Qu.:0.0891 3rd Qu.:0.1085
## Max. :0.5274 Max. :0.5672 Max. :0.6013
## NA's :1131 NA's :1123 NA's :1123
## Day181cd4_IL2_Wuhan.N Day181cd4_IL21_BA.4.5.S1 Day181cd4_IL21_BA.4.5.S2
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0052 Median :0.0037 Median :0.0027
## Mean :0.0162 Mean :0.0075 Mean :0.0083
## 3rd Qu.:0.0215 3rd Qu.:0.0094 3rd Qu.:0.0106
## Max. :0.1080 Max. :0.0791 Max. :0.0803
## NA's :1137 NA's :1128 NA's :1131
## Day181cd4_IL21_COV2.CON.S1 Day181cd4_IL21_COV2.CON.S2 Day181cd4_IL21_Wuhan.N
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0033 Median :0.0028 Median :0.0010
## Mean :0.0085 Mean :0.0087 Mean :0.0038
## 3rd Qu.:0.0097 3rd Qu.:0.0099 3rd Qu.:0.0047
## Max. :0.0773 Max. :0.0998 Max. :0.0248
## NA's :1123 NA's :1123 NA's :1137
## Day181cd4_IL4.154_BA.4.5.S1 Day181cd4_IL4.154_BA.4.5.S2
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010
## Mean :0.0017 Mean :0.0015
## 3rd Qu.:0.0010 3rd Qu.:0.0010
## Max. :0.0255 Max. :0.0148
## NA's :1128 NA's :1131
## Day181cd4_IL4.154_COV2.CON.S1 Day181cd4_IL4.154_COV2.CON.S2
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010
## Mean :0.0019 Mean :0.0020
## 3rd Qu.:0.0013 3rd Qu.:0.0012
## Max. :0.0196 Max. :0.0201
## NA's :1123 NA's :1123
## Day181cd4_IL4.154_Wuhan.N Day181cd4_IL4.IL5.IL13.154_BA.4.5.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010
## Mean :0.0010 Mean :0.0019
## 3rd Qu.:0.0010 3rd Qu.:0.0012
## Max. :0.0028 Max. :0.0300
## NA's :1137 NA's :1128
## Day181cd4_IL4.IL5.IL13.154_BA.4.5.S2 Day181cd4_IL4.IL5.IL13.154_COV2.CON.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010
## Mean :0.0017 Mean :0.0023
## 3rd Qu.:0.0011 3rd Qu.:0.0015
## Max. :0.0202 Max. :0.0196
## NA's :1131 NA's :1123
## Day181cd4_IL4.IL5.IL13.154_COV2.CON.S2 Day181cd4_IL4.IL5.IL13.154_Wuhan.N
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010
## Mean :0.0023 Mean :0.0011
## 3rd Qu.:0.0015 3rd Qu.:0.0010
## Max. :0.0254 Max. :0.0041
## NA's :1123 NA's :1137
## Day181cd4_IL5.IL13.154_BA.4.5.S1 Day181cd4_IL5.IL13.154_BA.4.5.S2
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010
## Mean :0.0014 Mean :0.0013
## 3rd Qu.:0.0010 3rd Qu.:0.0010
## Max. :0.0135 Max. :0.0121
## NA's :1128 NA's :1131
## Day181cd4_IL5.IL13.154_COV2.CON.S1 Day181cd4_IL5.IL13.154_COV2.CON.S2
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0010 Median :0.0010
## Mean :0.0016 Mean :0.0017
## 3rd Qu.:0.0010 3rd Qu.:0.0010
## Max. :0.0142 Max. :0.0173
## NA's :1123 NA's :1123
## Day181cd4_IL5.IL13.154_Wuhan.N Day181cd4_TNFa_BA.4.5.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0161
## Median :0.0010 Median :0.0416
## Mean :0.0010 Mean :0.0844
## 3rd Qu.:0.0010 3rd Qu.:0.0993
## Max. :0.0029 Max. :0.5945
## NA's :1137 NA's :1128
## Day181cd4_TNFa_BA.4.5.S2 Day181cd4_TNFa_COV2.CON.S1 Day181cd4_TNFa_COV2.CON.S2
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0247 1st Qu.:0.0178 1st Qu.:0.0282
## Median :0.0616 Median :0.0452 Median :0.0663
## Mean :0.0906 Mean :0.0921 Mean :0.1051
## 3rd Qu.:0.1078 3rd Qu.:0.1076 3rd Qu.:0.1264
## Max. :0.6823 Max. :0.7079 Max. :0.7825
## NA's :1131 NA's :1123 NA's :1123
## Day181cd4_TNFa_Wuhan.N Day181cd8_IFNg_BA.4.5.S1 Day181cd8_IFNg_BA.4.5.S2
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0100 1st Qu.:0.0029
## Median :0.0010 Median :0.0515 Median :0.0118
## Mean :0.0130 Mean :0.4266 Mean :0.0640
## 3rd Qu.:0.0111 3rd Qu.:0.2807 3rd Qu.:0.0357
## Max. :0.1425 Max. :8.9409 Max. :2.2259
## NA's :1137 NA's :1129 NA's :1131
## Day181cd8_IFNg_COV2.CON.S1 Day181cd8_IFNg_COV2.CON.S2 Day181cd8_IFNg_Wuhan.N
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0093 1st Qu.:0.0020 1st Qu.:0.0010
## Median :0.0741 Median :0.0135 Median :0.0049
## Mean :0.3871 Mean :0.0757 Mean :0.0147
## 3rd Qu.:0.2653 3rd Qu.:0.0410 3rd Qu.:0.0138
## Max. :6.2853 Max. :2.8235 Max. :0.1989
## NA's :1123 NA's :1123 NA's :1137
## Day181cd8_IFNg.IL2_BA.4.5.S1 Day181cd8_IFNg.IL2_BA.4.5.S2
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0100 1st Qu.:0.0018
## Median :0.0515 Median :0.0122
## Mean :0.4280 Mean :0.0641
## 3rd Qu.:0.2819 3rd Qu.:0.0354
## Max. :8.9409 Max. :2.2227
## NA's :1129 NA's :1131
## Day181cd8_IFNg.IL2_COV2.CON.S1 Day181cd8_IFNg.IL2_COV2.CON.S2
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0077 1st Qu.:0.0027
## Median :0.0762 Median :0.0140
## Mean :0.3886 Mean :0.0764
## 3rd Qu.:0.2635 3rd Qu.:0.0430
## Max. :6.2827 Max. :2.8204
## NA's :1123 NA's :1123
## Day181cd8_IFNg.IL2_Wuhan.N Day181cd8_IFNg.IL2.TNFa_BA.4.5.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0168
## Median :0.0053 Median :0.0618
## Mean :0.0152 Mean :0.4440
## 3rd Qu.:0.0148 3rd Qu.:0.2570
## Max. :0.1989 Max. :9.1226
## NA's :1137 NA's :1129
## Day181cd8_IFNg.IL2.TNFa_BA.4.5.S2 Day181cd8_IFNg.IL2.TNFa_COV2.CON.S1
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0011 1st Qu.:0.0096
## Median :0.0159 Median :0.0880
## Mean :0.0695 Mean :0.4036
## 3rd Qu.:0.0423 3rd Qu.:0.2629
## Max. :2.2399 Max. :6.3817
## NA's :1131 NA's :1123
## Day181cd8_IFNg.IL2.TNFa_COV2.CON.S2 Day181cd8_IFNg.IL2.TNFa_Wuhan.N
## Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0015 1st Qu.:0.0010
## Median :0.0157 Median :0.0049
## Mean :0.0810 Mean :0.0159
## 3rd Qu.:0.0487 3rd Qu.:0.0182
## Max. :2.8274 Max. :0.2013
## NA's :1123 NA's :1137
## Day181cd8_IL2_BA.4.5.S1 Day181cd8_IL2_BA.4.5.S2 Day181cd8_IL2_COV2.CON.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010 1st Qu.:0.0010
## Median :0.0065 Median :0.0010 Median :0.0079
## Mean :0.0315 Mean :0.0045 Mean :0.0270
## 3rd Qu.:0.0280 3rd Qu.:0.0064 3rd Qu.:0.0270
## Max. :0.6543 Max. :0.0331 Max. :0.2259
## NA's :1129 NA's :1131 NA's :1123
## Day181cd8_IL2_COV2.CON.S2 Day181cd8_IL2_Wuhan.N Day181cd8_TNFa_BA.4.5.S1
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0010 1st Qu.:0.0137
## Median :0.0020 Median :0.0010 Median :0.0446
## Mean :0.0061 Mean :0.0050 Mean :0.3630
## 3rd Qu.:0.0075 3rd Qu.:0.0057 3rd Qu.:0.1751
## Max. :0.0711 Max. :0.0592 Max. :8.0694
## NA's :1123 NA's :1137 NA's :1129
## Day181cd8_TNFa_BA.4.5.S2 Day181cd8_TNFa_COV2.CON.S1 Day181cd8_TNFa_COV2.CON.S2
## Min. :0.0010 Min. :0.0010 Min. :0.0010
## 1st Qu.:0.0010 1st Qu.:0.0049 1st Qu.:0.0010
## Median :0.0081 Median :0.0506 Median :0.0094
## Mean :0.0518 Mean :0.3073 Mean :0.0602
## 3rd Qu.:0.0332 3rd Qu.:0.1828 3rd Qu.:0.0320
## Max. :1.6552 Max. :5.3517 Max. :2.1769
## NA's :1131 NA's :1123 NA's :1123
## Day181cd8_TNFa_Wuhan.N Bcd4_154_BA.4.5.S1_resp Bcd4_154_BA.4.5.S2_resp
## Min. :0.0010 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0010 1st Qu.:0.0000 1st Qu.:1.0000
## Median :0.0010 Median :1.0000 Median :1.0000
## Mean :0.0080 Mean :0.6614 Mean :0.7989
## 3rd Qu.:0.0104 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :0.1658 Max. :1.0000 Max. :1.0000
## NA's :1137 NA's :692 NA's :710
## Bcd4_154_COV2.CON.S1_resp Bcd4_154_COV2.CON.S2_resp Bcd4_154_Wuhan.N_resp
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:0.0000
## Median :1.0000 Median :1.0000 Median :0.0000
## Mean :0.8038 Mean :0.8212 Mean :0.1335
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :686 NA's :686 NA's :745
## Bcd4_CXCR5.154_BA.4.5.S1_resp Bcd4_CXCR5.154_BA.4.5.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.0071 Mean :0.0091
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :700 NA's :713
## Bcd4_CXCR5.154_COV2.CON.S1_resp Bcd4_CXCR5.154_COV2.CON.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.0243 Mean :0.0104
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :687 NA's :686
## Bcd4_CXCR5.154_Wuhan.N_resp Bcd4_CXCR5.IL21_BA.4.5.S1_resp
## Min. :0.0000 Min. :0
## 1st Qu.:0.0000 1st Qu.:0
## Median :0.0000 Median :0
## Mean :0.0039 Mean :0
## 3rd Qu.:0.0000 3rd Qu.:0
## Max. :1.0000 Max. :0
## NA's :750 NA's :700
## Bcd4_CXCR5.IL21_BA.4.5.S2_resp Bcd4_CXCR5.IL21_COV2.CON.S1_resp
## Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0
## Median :0 Median :0
## Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0
## NA's :713 NA's :687
## Bcd4_CXCR5.IL21_COV2.CON.S2_resp Bcd4_CXCR5.IL21_Wuhan.N_resp
## Min. :0 Min. :0.000
## 1st Qu.:0 1st Qu.:0.000
## Median :0 Median :0.000
## Mean :0 Mean :0.002
## 3rd Qu.:0 3rd Qu.:0.000
## Max. :0 Max. :1.000
## NA's :686 NA's :750
## Bcd4_IFNg_BA.4.5.S1_resp Bcd4_IFNg_BA.4.5.S2_resp Bcd4_IFNg_COV2.CON.S1_resp
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :1.0000 Median :1.0000
## Mean :0.5105 Mean :0.6775 Mean :0.6545
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :692 NA's :710 NA's :686
## Bcd4_IFNg_COV2.CON.S2_resp Bcd4_IFNg_Wuhan.N_resp Bcd4_IFNg.IL2_BA.4.5.S1_resp
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :0.0000 Median :1.0000
## Mean :0.7326 Mean :0.0832 Mean :0.5912
## 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :686 NA's :745 NA's :692
## Bcd4_IFNg.IL2_BA.4.5.S2_resp Bcd4_IFNg.IL2_COV2.CON.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :1.0000
## Mean :0.7246 Mean :0.7101
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :710 NA's :686
## Bcd4_IFNg.IL2_COV2.CON.S2_resp Bcd4_IFNg.IL2_Wuhan.N_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:0.0000
## Median :1.0000 Median :0.0000
## Mean :0.7691 Mean :0.1103
## 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :686 NA's :745
## Bcd4_IFNg.IL2.154_BA.4.5.S1_resp Bcd4_IFNg.IL2.154_BA.4.5.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :1.0000
## Mean :0.6211 Mean :0.7428
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :692 NA's :710
## Bcd4_IFNg.IL2.154_COV2.CON.S1_resp Bcd4_IFNg.IL2.154_COV2.CON.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:1.0000
## Median :1.0000 Median :1.0000
## Mean :0.7274 Mean :0.7691
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :686 NA's :686
## Bcd4_IFNg.IL2.154_Wuhan.N_resp Bcd4_IL17a_BA.4.5.S1_resp
## Min. :0.000 Min. :0
## 1st Qu.:0.000 1st Qu.:0
## Median :0.000 Median :0
## Mean :0.118 Mean :0
## 3rd Qu.:0.000 3rd Qu.:0
## Max. :1.000 Max. :0
## NA's :745 NA's :692
## Bcd4_IL17a_BA.4.5.S2_resp Bcd4_IL17a_COV2.CON.S1_resp
## Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0
## Median :0 Median :0
## Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0
## NA's :710 NA's :686
## Bcd4_IL17a_COV2.CON.S2_resp Bcd4_IL17a_Wuhan.N_resp Bcd4_IL2_BA.4.5.S1_resp
## Min. :0 Min. :0 Min. :0.0000
## 1st Qu.:0 1st Qu.:0 1st Qu.:0.0000
## Median :0 Median :0 Median :1.0000
## Mean :0 Mean :0 Mean :0.5351
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:1.0000
## Max. :0 Max. :0 Max. :1.0000
## NA's :686 NA's :745 NA's :692
## Bcd4_IL2_BA.4.5.S2_resp Bcd4_IL2_COV2.CON.S1_resp Bcd4_IL2_COV2.CON.S2_resp
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :1.0000 Median :1.0000
## Mean :0.6467 Mean :0.6458 Mean :0.6997
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :710 NA's :686 NA's :686
## Bcd4_IL2_Wuhan.N_resp Bcd4_IL21_BA.4.5.S1_resp Bcd4_IL21_BA.4.5.S2_resp
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.0851 Mean :0.0053 Mean :0.0054
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :745 NA's :692 NA's :710
## Bcd4_IL21_COV2.CON.S1_resp Bcd4_IL21_COV2.CON.S2_resp Bcd4_IL21_Wuhan.N_resp
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.0104 Mean :0.0104 Mean :0.0039
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :686 NA's :686 NA's :745
## Bcd4_IL4.154_BA.4.5.S1_resp Bcd4_IL4.154_BA.4.5.S2_resp
## Min. :0 Min. :0.0000
## 1st Qu.:0 1st Qu.:0.0000
## Median :0 Median :0.0000
## Mean :0 Mean :0.0018
## 3rd Qu.:0 3rd Qu.:0.0000
## Max. :0 Max. :1.0000
## NA's :692 NA's :710
## Bcd4_IL4.154_COV2.CON.S1_resp Bcd4_IL4.154_COV2.CON.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.0052 Mean :0.0035
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :686 NA's :686
## Bcd4_IL4.154_Wuhan.N_resp Bcd4_IL4.IL5.IL13.154_BA.4.5.S1_resp
## Min. :0 Min. :0.0000
## 1st Qu.:0 1st Qu.:0.0000
## Median :0 Median :0.0000
## Mean :0 Mean :0.0035
## 3rd Qu.:0 3rd Qu.:0.0000
## Max. :0 Max. :1.0000
## NA's :745 NA's :692
## Bcd4_IL4.IL5.IL13.154_BA.4.5.S2_resp Bcd4_IL4.IL5.IL13.154_COV2.CON.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.0072 Mean :0.0069
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :710 NA's :686
## Bcd4_IL4.IL5.IL13.154_COV2.CON.S2_resp Bcd4_IL4.IL5.IL13.154_Wuhan.N_resp
## Min. :0.0000 Min. :0
## 1st Qu.:0.0000 1st Qu.:0
## Median :0.0000 Median :0
## Mean :0.0069 Mean :0
## 3rd Qu.:0.0000 3rd Qu.:0
## Max. :1.0000 Max. :0
## NA's :686 NA's :745
## Bcd4_IL5.IL13.154_BA.4.5.S1_resp Bcd4_IL5.IL13.154_BA.4.5.S2_resp
## Min. :0.0000 Min. :0
## 1st Qu.:0.0000 1st Qu.:0
## Median :0.0000 Median :0
## Mean :0.0035 Mean :0
## 3rd Qu.:0.0000 3rd Qu.:0
## Max. :1.0000 Max. :0
## NA's :692 NA's :710
## Bcd4_IL5.IL13.154_COV2.CON.S1_resp Bcd4_IL5.IL13.154_COV2.CON.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.0017 Mean :0.0017
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :686 NA's :686
## Bcd4_IL5.IL13.154_Wuhan.N_resp Bcd4_TNFa_BA.4.5.S1_resp
## Min. :0 Min. :0.0000
## 1st Qu.:0 1st Qu.:0.0000
## Median :0 Median :0.0000
## Mean :0 Mean :0.3491
## 3rd Qu.:0 3rd Qu.:1.0000
## Max. :0 Max. :1.0000
## NA's :745 NA's :692
## Bcd4_TNFa_BA.4.5.S2_resp Bcd4_TNFa_COV2.CON.S1_resp Bcd4_TNFa_COV2.CON.S2_resp
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.4312 Mean :0.4219 Mean :0.4774
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :710 NA's :686 NA's :686
## Bcd4_TNFa_Wuhan.N_resp Bcd8_IFNg_BA.4.5.S1_resp Bcd8_IFNg_BA.4.5.S2_resp
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.0387 Mean :0.3662 Mean :0.1866
## 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :745 NA's :694 NA's :710
## Bcd8_IFNg_COV2.CON.S1_resp Bcd8_IFNg_COV2.CON.S2_resp Bcd8_IFNg_Wuhan.N_resp
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.3785 Mean :0.1528 Mean :0.0349
## 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :686 NA's :686 NA's :746
## Bcd8_IFNg.IL2_BA.4.5.S1_resp Bcd8_IFNg.IL2_BA.4.5.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.3592 Mean :0.1793
## 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :694 NA's :710
## Bcd8_IFNg.IL2_COV2.CON.S1_resp Bcd8_IFNg.IL2_COV2.CON.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.3767 Mean :0.1424
## 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :686 NA's :686
## Bcd8_IFNg.IL2_Wuhan.N_resp Bcd8_IFNg.IL2.TNFa_BA.4.5.S1_resp
## Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.000
## Median :0.0000 Median :0.000
## Mean :0.0349 Mean :0.338
## 3rd Qu.:0.0000 3rd Qu.:1.000
## Max. :1.0000 Max. :1.000
## NA's :746 NA's :694
## Bcd8_IFNg.IL2.TNFa_BA.4.5.S2_resp Bcd8_IFNg.IL2.TNFa_COV2.CON.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.1721 Mean :0.3576
## 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :710 NA's :686
## Bcd8_IFNg.IL2.TNFa_COV2.CON.S2_resp Bcd8_IFNg.IL2.TNFa_Wuhan.N_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.1319 Mean :0.0252
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :686 NA's :746
## Bcd8_IL2_BA.4.5.S1_resp Bcd8_IL2_BA.4.5.S2_resp Bcd8_IL2_COV2.CON.S1_resp
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.1144 Mean :0.0236 Mean :0.1059
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :694 NA's :710 NA's :686
## Bcd8_IL2_COV2.CON.S2_resp Bcd8_IL2_Wuhan.N_resp Bcd8_TNFa_BA.4.5.S1_resp
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.0278 Mean :0.0155 Mean :0.2975
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :686 NA's :746 NA's :694
## Bcd8_TNFa_BA.4.5.S2_resp Bcd8_TNFa_COV2.CON.S1_resp Bcd8_TNFa_COV2.CON.S2_resp
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.1322 Mean :0.2847 Mean :0.1059
## 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :710 NA's :686 NA's :686
## Bcd8_TNFa_Wuhan.N_resp Day15cd4_154_BA.4.5.S1_resp Day15cd4_154_BA.4.5.S2_resp
## Min. :0.0000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:1.000 1st Qu.:1.0000
## Median :0.0000 Median :1.000 Median :1.0000
## Mean :0.0116 Mean :0.884 Mean :0.9159
## 3rd Qu.:0.0000 3rd Qu.:1.000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.000 Max. :1.0000
## NA's :746 NA's :693 NA's :703
## Day15cd4_154_COV2.CON.S1_resp Day15cd4_154_COV2.CON.S2_resp
## Min. :0.000 Min. :0.0000
## 1st Qu.:1.000 1st Qu.:1.0000
## Median :1.000 Median :1.0000
## Mean :0.934 Mean :0.9167
## 3rd Qu.:1.000 3rd Qu.:1.0000
## Max. :1.000 Max. :1.0000
## NA's :686 NA's :686
## Day15cd4_154_Wuhan.N_resp Day15cd4_CXCR5.154_BA.4.5.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.1394 Mean :0.0677
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :717 NA's :701
## Day15cd4_CXCR5.154_BA.4.5.S2_resp Day15cd4_CXCR5.154_COV2.CON.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.0522 Mean :0.1304
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :706 NA's :687
## Day15cd4_CXCR5.154_COV2.CON.S2_resp Day15cd4_CXCR5.154_Wuhan.N_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.0799 Mean :0.0019
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :686 NA's :723
## Day15cd4_CXCR5.IL21_BA.4.5.S1_resp Day15cd4_CXCR5.IL21_BA.4.5.S2_resp
## Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0
## Median :0 Median :0
## Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0
## NA's :701 NA's :706
## Day15cd4_CXCR5.IL21_COV2.CON.S1_resp Day15cd4_CXCR5.IL21_COV2.CON.S2_resp
## Min. :0.0000 Min. :0
## 1st Qu.:0.0000 1st Qu.:0
## Median :0.0000 Median :0
## Mean :0.0035 Mean :0
## 3rd Qu.:0.0000 3rd Qu.:0
## Max. :1.0000 Max. :0
## NA's :687 NA's :686
## Day15cd4_CXCR5.IL21_Wuhan.N_resp Day15cd4_IFNg_BA.4.5.S1_resp
## Min. :0 Min. :0.0000
## 1st Qu.:0 1st Qu.:1.0000
## Median :0 Median :1.0000
## Mean :0 Mean :0.8032
## 3rd Qu.:0 3rd Qu.:1.0000
## Max. :0 Max. :1.0000
## NA's :723 NA's :693
## Day15cd4_IFNg_BA.4.5.S2_resp Day15cd4_IFNg_COV2.CON.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:1.0000
## Median :1.0000 Median :1.0000
## Mean :0.8962 Mean :0.8715
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :703 NA's :686
## Day15cd4_IFNg_COV2.CON.S2_resp Day15cd4_IFNg_Wuhan.N_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:0.0000
## Median :1.0000 Median :0.0000
## Mean :0.8958 Mean :0.0936
## 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :686 NA's :717
## Day15cd4_IFNg.IL2_BA.4.5.S1_resp Day15cd4_IFNg.IL2_BA.4.5.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:1.0000
## Median :1.0000 Median :1.0000
## Mean :0.8172 Mean :0.8998
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :693 NA's :703
## Day15cd4_IFNg.IL2_COV2.CON.S1_resp Day15cd4_IFNg.IL2_COV2.CON.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:1.0000
## Median :1.0000 Median :1.0000
## Mean :0.8993 Mean :0.9028
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :686 NA's :686
## Day15cd4_IFNg.IL2_Wuhan.N_resp Day15cd4_IFNg.IL2.154_BA.4.5.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:1.0000
## Median :0.0000 Median :1.0000
## Mean :0.1064 Mean :0.8383
## 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :717 NA's :693
## Day15cd4_IFNg.IL2.154_BA.4.5.S2_resp Day15cd4_IFNg.IL2.154_COV2.CON.S1_resp
## Min. :0.000 Min. :0.0000
## 1st Qu.:1.000 1st Qu.:1.0000
## Median :1.000 Median :1.0000
## Mean :0.898 Mean :0.9097
## 3rd Qu.:1.000 3rd Qu.:1.0000
## Max. :1.000 Max. :1.0000
## NA's :703 NA's :686
## Day15cd4_IFNg.IL2.154_COV2.CON.S2_resp Day15cd4_IFNg.IL2.154_Wuhan.N_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:0.0000
## Median :1.0000 Median :0.0000
## Mean :0.9045 Mean :0.1156
## 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :686 NA's :717
## Day15cd4_IL17a_BA.4.5.S1_resp Day15cd4_IL17a_BA.4.5.S2_resp
## Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0
## Median :0 Median :0
## Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0
## NA's :693 NA's :703
## Day15cd4_IL17a_COV2.CON.S1_resp Day15cd4_IL17a_COV2.CON.S2_resp
## Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0
## Median :0 Median :0
## Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0
## NA's :686 NA's :686
## Day15cd4_IL17a_Wuhan.N_resp Day15cd4_IL2_BA.4.5.S1_resp
## Min. :0 Min. :0.0000
## 1st Qu.:0 1st Qu.:0.0000
## Median :0 Median :1.0000
## Mean :0 Mean :0.6819
## 3rd Qu.:0 3rd Qu.:1.0000
## Max. :0 Max. :1.0000
## NA's :717 NA's :693
## Day15cd4_IL2_BA.4.5.S2_resp Day15cd4_IL2_COV2.CON.S1_resp
## Min. :0.00 Min. :0.0000
## 1st Qu.:1.00 1st Qu.:1.0000
## Median :1.00 Median :1.0000
## Mean :0.78 Mean :0.7778
## 3rd Qu.:1.00 3rd Qu.:1.0000
## Max. :1.00 Max. :1.0000
## NA's :703 NA's :686
## Day15cd4_IL2_COV2.CON.S2_resp Day15cd4_IL2_Wuhan.N_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:0.0000
## Median :1.0000 Median :0.0000
## Mean :0.7969 Mean :0.0844
## 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :686 NA's :717
## Day15cd4_IL21_BA.4.5.S1_resp Day15cd4_IL21_BA.4.5.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.1195 Mean :0.1914
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :693 NA's :703
## Day15cd4_IL21_COV2.CON.S1_resp Day15cd4_IL21_COV2.CON.S2_resp
## Min. :0.000 Min. :0.0000
## 1st Qu.:0.000 1st Qu.:0.0000
## Median :0.000 Median :0.0000
## Mean :0.184 Mean :0.2257
## 3rd Qu.:0.000 3rd Qu.:0.0000
## Max. :1.000 Max. :1.0000
## NA's :686 NA's :686
## Day15cd4_IL21_Wuhan.N_resp Day15cd4_IL4.154_BA.4.5.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.0055 Mean :0.0088
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :717 NA's :693
## Day15cd4_IL4.154_BA.4.5.S2_resp Day15cd4_IL4.154_COV2.CON.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.0054 Mean :0.0243
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :703 NA's :686
## Day15cd4_IL4.154_COV2.CON.S2_resp Day15cd4_IL4.154_Wuhan.N_resp
## Min. :0.0000 Min. :0
## 1st Qu.:0.0000 1st Qu.:0
## Median :0.0000 Median :0
## Mean :0.0087 Mean :0
## 3rd Qu.:0.0000 3rd Qu.:0
## Max. :1.0000 Max. :0
## NA's :686 NA's :717
## Day15cd4_IL4.IL5.IL13.154_BA.4.5.S1_resp
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.0193
## 3rd Qu.:0.0000
## Max. :1.0000
## NA's :693
## Day15cd4_IL4.IL5.IL13.154_BA.4.5.S2_resp
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.0179
## 3rd Qu.:0.0000
## Max. :1.0000
## NA's :703
## Day15cd4_IL4.IL5.IL13.154_COV2.CON.S1_resp
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.0365
## 3rd Qu.:0.0000
## Max. :1.0000
## NA's :686
## Day15cd4_IL4.IL5.IL13.154_COV2.CON.S2_resp
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.0226
## 3rd Qu.:0.0000
## Max. :1.0000
## NA's :686
## Day15cd4_IL4.IL5.IL13.154_Wuhan.N_resp Day15cd4_IL5.IL13.154_BA.4.5.S1_resp
## Min. :0 Min. :0.0000
## 1st Qu.:0 1st Qu.:0.0000
## Median :0 Median :0.0000
## Mean :0 Mean :0.0053
## 3rd Qu.:0 3rd Qu.:0.0000
## Max. :0 Max. :1.0000
## NA's :717 NA's :693
## Day15cd4_IL5.IL13.154_BA.4.5.S2_resp Day15cd4_IL5.IL13.154_COV2.CON.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.0054 Mean :0.0122
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :703 NA's :686
## Day15cd4_IL5.IL13.154_COV2.CON.S2_resp Day15cd4_IL5.IL13.154_Wuhan.N_resp
## Min. :0.0000 Min. :0
## 1st Qu.:0.0000 1st Qu.:0
## Median :0.0000 Median :0
## Mean :0.0035 Mean :0
## 3rd Qu.:0.0000 3rd Qu.:0
## Max. :1.0000 Max. :0
## NA's :686 NA's :717
## Day15cd4_TNFa_BA.4.5.S1_resp Day15cd4_TNFa_BA.4.5.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :1.0000
## Mean :0.5431 Mean :0.6279
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :693 NA's :703
## Day15cd4_TNFa_COV2.CON.S1_resp Day15cd4_TNFa_COV2.CON.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :1.0000
## Mean :0.5972 Mean :0.6545
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :686 NA's :686
## Day15cd4_TNFa_Wuhan.N_resp Day15cd8_IFNg_BA.4.5.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.0275 Mean :0.4586
## 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :717 NA's :695
## Day15cd8_IFNg_BA.4.5.S2_resp Day15cd8_IFNg_COV2.CON.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.2576 Mean :0.4991
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :703 NA's :687
## Day15cd8_IFNg_COV2.CON.S2_resp Day15cd8_IFNg_Wuhan.N_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.2326 Mean :0.0331
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :686 NA's :719
## Day15cd8_IFNg.IL2_BA.4.5.S1_resp Day15cd8_IFNg.IL2_BA.4.5.S2_resp
## Min. :0.000 Min. :0.0000
## 1st Qu.:0.000 1st Qu.:0.0000
## Median :0.000 Median :0.0000
## Mean :0.448 Mean :0.2504
## 3rd Qu.:1.000 3rd Qu.:0.5000
## Max. :1.000 Max. :1.0000
## NA's :695 NA's :703
## Day15cd8_IFNg.IL2_COV2.CON.S1_resp Day15cd8_IFNg.IL2_COV2.CON.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.4922 Mean :0.2257
## 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :687 NA's :686
## Day15cd8_IFNg.IL2_Wuhan.N_resp Day15cd8_IFNg.IL2.TNFa_BA.4.5.S1_resp
## Min. :0.000 Min. :0.0000
## 1st Qu.:0.000 1st Qu.:0.0000
## Median :0.000 Median :0.0000
## Mean :0.035 Mean :0.4444
## 3rd Qu.:0.000 3rd Qu.:1.0000
## Max. :1.000 Max. :1.0000
## NA's :719 NA's :695
## Day15cd8_IFNg.IL2.TNFa_BA.4.5.S2_resp Day15cd8_IFNg.IL2.TNFa_COV2.CON.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.2326 Mean :0.4661
## 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :703 NA's :687
## Day15cd8_IFNg.IL2.TNFa_COV2.CON.S2_resp Day15cd8_IFNg.IL2.TNFa_Wuhan.N_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.2101 Mean :0.0276
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :686 NA's :719
## Day15cd8_IL2_BA.4.5.S1_resp Day15cd8_IL2_BA.4.5.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.1464 Mean :0.0286
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :695 NA's :703
## Day15cd8_IL2_COV2.CON.S1_resp Day15cd8_IL2_COV2.CON.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.1374 Mean :0.0365
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :687 NA's :686
## Day15cd8_IL2_Wuhan.N_resp Day15cd8_TNFa_BA.4.5.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.0055 Mean :0.3474
## 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :719 NA's :695
## Day15cd8_TNFa_BA.4.5.S2_resp Day15cd8_TNFa_COV2.CON.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.1646 Mean :0.3478
## 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :703 NA's :687
## Day15cd8_TNFa_COV2.CON.S2_resp Day15cd8_TNFa_Wuhan.N_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.1372 Mean :0.0074
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :686 NA's :719
## Day91cd4_154_BA.4.5.S1_resp Day91cd4_154_BA.4.5.S2_resp
## Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.000
## Median :1.0000 Median :1.000
## Mean :0.6529 Mean :0.744
## 3rd Qu.:1.0000 3rd Qu.:1.000
## Max. :1.0000 Max. :1.000
## NA's :1092 NA's :1094
## Day91cd4_154_COV2.CON.S1_resp Day91cd4_154_COV2.CON.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:1.0000
## Median :1.0000 Median :1.0000
## Mean :0.7989 Mean :0.8046
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :1088 NA's :1088
## Day91cd4_154_Wuhan.N_resp Day91cd4_CXCR5.154_BA.4.5.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.1783 Mean :0.0184
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :1105 NA's :1099
## Day91cd4_CXCR5.154_BA.4.5.S2_resp Day91cd4_CXCR5.154_COV2.CON.S1_resp
## Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.000
## Median :0.0000 Median :0.000
## Mean :0.0181 Mean :0.023
## 3rd Qu.:0.0000 3rd Qu.:0.000
## Max. :1.0000 Max. :1.000
## NA's :1096 NA's :1088
## Day91cd4_CXCR5.154_COV2.CON.S2_resp Day91cd4_CXCR5.154_Wuhan.N_resp
## Min. :0.0000 Min. :0
## 1st Qu.:0.0000 1st Qu.:0
## Median :0.0000 Median :0
## Mean :0.0172 Mean :0
## 3rd Qu.:0.0000 3rd Qu.:0
## Max. :1.0000 Max. :0
## NA's :1088 NA's :1111
## Day91cd4_CXCR5.IL21_BA.4.5.S1_resp Day91cd4_CXCR5.IL21_BA.4.5.S2_resp
## Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0
## Median :0 Median :0
## Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0
## NA's :1099 NA's :1096
## Day91cd4_CXCR5.IL21_COV2.CON.S1_resp Day91cd4_CXCR5.IL21_COV2.CON.S2_resp
## Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0
## Median :0 Median :0
## Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0
## NA's :1088 NA's :1088
## Day91cd4_CXCR5.IL21_Wuhan.N_resp Day91cd4_IFNg_BA.4.5.S1_resp
## Min. :0 Min. :0.0000
## 1st Qu.:0 1st Qu.:0.0000
## Median :0 Median :1.0000
## Mean :0 Mean :0.6235
## 3rd Qu.:0 3rd Qu.:1.0000
## Max. :0 Max. :1.0000
## NA's :1111 NA's :1092
## Day91cd4_IFNg_BA.4.5.S2_resp Day91cd4_IFNg_COV2.CON.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :1.0000
## Mean :0.7143 Mean :0.7414
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :1094 NA's :1088
## Day91cd4_IFNg_COV2.CON.S2_resp Day91cd4_IFNg_Wuhan.N_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:0.0000
## Median :1.0000 Median :0.0000
## Mean :0.7529 Mean :0.1465
## 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :1088 NA's :1105
## Day91cd4_IFNg.IL2_BA.4.5.S1_resp Day91cd4_IFNg.IL2_BA.4.5.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :1.0000
## Mean :0.6353 Mean :0.7143
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :1092 NA's :1094
## Day91cd4_IFNg.IL2_COV2.CON.S1_resp Day91cd4_IFNg.IL2_COV2.CON.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:1.0000
## Median :1.0000 Median :1.0000
## Mean :0.7644 Mean :0.7759
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :1088 NA's :1088
## Day91cd4_IFNg.IL2_Wuhan.N_resp Day91cd4_IFNg.IL2.154_BA.4.5.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :1.0000
## Mean :0.1783 Mean :0.6294
## 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :1105 NA's :1092
## Day91cd4_IFNg.IL2.154_BA.4.5.S2_resp Day91cd4_IFNg.IL2.154_COV2.CON.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:1.0000
## Median :1.0000 Median :1.0000
## Mean :0.6905 Mean :0.7644
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :1094 NA's :1088
## Day91cd4_IFNg.IL2.154_COV2.CON.S2_resp Day91cd4_IFNg.IL2.154_Wuhan.N_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:0.0000
## Median :1.0000 Median :0.0000
## Mean :0.7701 Mean :0.1656
## 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :1088 NA's :1105
## Day91cd4_IL17a_BA.4.5.S1_resp Day91cd4_IL17a_BA.4.5.S2_resp
## Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0
## Median :0 Median :0
## Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0
## NA's :1092 NA's :1094
## Day91cd4_IL17a_COV2.CON.S1_resp Day91cd4_IL17a_COV2.CON.S2_resp
## Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0
## Median :0 Median :0
## Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0
## NA's :1088 NA's :1088
## Day91cd4_IL17a_Wuhan.N_resp Day91cd4_IL2_BA.4.5.S1_resp
## Min. :0 Min. :0.0000
## 1st Qu.:0 1st Qu.:0.0000
## Median :0 Median :1.0000
## Mean :0 Mean :0.5176
## 3rd Qu.:0 3rd Qu.:1.0000
## Max. :0 Max. :1.0000
## NA's :1105 NA's :1092
## Day91cd4_IL2_BA.4.5.S2_resp Day91cd4_IL2_COV2.CON.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :1.0000
## Mean :0.5952 Mean :0.6379
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :1094 NA's :1088
## Day91cd4_IL2_COV2.CON.S2_resp Day91cd4_IL2_Wuhan.N_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :0.0000
## Mean :0.6494 Mean :0.1338
## 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :1088 NA's :1105
## Day91cd4_IL21_BA.4.5.S1_resp Day91cd4_IL21_BA.4.5.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.0176 Mean :0.0119
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :1092 NA's :1094
## Day91cd4_IL21_COV2.CON.S1_resp Day91cd4_IL21_COV2.CON.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.0172 Mean :0.0172
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :1088 NA's :1088
## Day91cd4_IL21_Wuhan.N_resp Day91cd4_IL4.154_BA.4.5.S1_resp
## Min. :0 Min. :0.0000
## 1st Qu.:0 1st Qu.:0.0000
## Median :0 Median :0.0000
## Mean :0 Mean :0.0059
## 3rd Qu.:0 3rd Qu.:0.0000
## Max. :0 Max. :1.0000
## NA's :1105 NA's :1092
## Day91cd4_IL4.154_BA.4.5.S2_resp Day91cd4_IL4.154_COV2.CON.S1_resp
## Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0
## Median :0 Median :0
## Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0
## NA's :1094 NA's :1088
## Day91cd4_IL4.154_COV2.CON.S2_resp Day91cd4_IL4.154_Wuhan.N_resp
## Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0
## Median :0 Median :0
## Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0
## NA's :1088 NA's :1105
## Day91cd4_IL4.IL5.IL13.154_BA.4.5.S1_resp
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.0059
## 3rd Qu.:0.0000
## Max. :1.0000
## NA's :1092
## Day91cd4_IL4.IL5.IL13.154_BA.4.5.S2_resp
## Min. :0
## 1st Qu.:0
## Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
## NA's :1094
## Day91cd4_IL4.IL5.IL13.154_COV2.CON.S1_resp
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.0115
## 3rd Qu.:0.0000
## Max. :1.0000
## NA's :1088
## Day91cd4_IL4.IL5.IL13.154_COV2.CON.S2_resp
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.0115
## 3rd Qu.:0.0000
## Max. :1.0000
## NA's :1088
## Day91cd4_IL4.IL5.IL13.154_Wuhan.N_resp Day91cd4_IL5.IL13.154_BA.4.5.S1_resp
## Min. :0 Min. :0.0000
## 1st Qu.:0 1st Qu.:0.0000
## Median :0 Median :0.0000
## Mean :0 Mean :0.0059
## 3rd Qu.:0 3rd Qu.:0.0000
## Max. :0 Max. :1.0000
## NA's :1105 NA's :1092
## Day91cd4_IL5.IL13.154_BA.4.5.S2_resp Day91cd4_IL5.IL13.154_COV2.CON.S1_resp
## Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0
## Median :0 Median :0
## Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0
## NA's :1094 NA's :1088
## Day91cd4_IL5.IL13.154_COV2.CON.S2_resp Day91cd4_IL5.IL13.154_Wuhan.N_resp
## Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0
## Median :0 Median :0
## Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0
## NA's :1088 NA's :1105
## Day91cd4_TNFa_BA.4.5.S1_resp Day91cd4_TNFa_BA.4.5.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :1.0000
## Mean :0.3765 Mean :0.5298
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :1092 NA's :1094
## Day91cd4_TNFa_COV2.CON.S1_resp Day91cd4_TNFa_COV2.CON.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :1.0000
## Mean :0.4425 Mean :0.5115
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :1088 NA's :1088
## Day91cd4_TNFa_Wuhan.N_resp Day91cd8_IFNg_BA.4.5.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.0318 Mean :0.4793
## 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :1105 NA's :1093
## Day91cd8_IFNg_BA.4.5.S2_resp Day91cd8_IFNg_COV2.CON.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :1.0000
## Mean :0.2143 Mean :0.5402
## 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :1094 NA's :1088
## Day91cd8_IFNg_COV2.CON.S2_resp Day91cd8_IFNg_Wuhan.N_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.2069 Mean :0.0255
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :1088 NA's :1105
## Day91cd8_IFNg.IL2_BA.4.5.S1_resp Day91cd8_IFNg.IL2_BA.4.5.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.4734 Mean :0.2143
## 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :1093 NA's :1094
## Day91cd8_IFNg.IL2_COV2.CON.S1_resp Day91cd8_IFNg.IL2_COV2.CON.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :0.0000
## Mean :0.5517 Mean :0.2011
## 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :1088 NA's :1088
## Day91cd8_IFNg.IL2_Wuhan.N_resp Day91cd8_IFNg.IL2.TNFa_BA.4.5.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.0191 Mean :0.4675
## 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :1105 NA's :1093
## Day91cd8_IFNg.IL2.TNFa_BA.4.5.S2_resp Day91cd8_IFNg.IL2.TNFa_COV2.CON.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :1.0000
## Mean :0.1786 Mean :0.5115
## 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :1094 NA's :1088
## Day91cd8_IFNg.IL2.TNFa_COV2.CON.S2_resp Day91cd8_IFNg.IL2.TNFa_Wuhan.N_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.1782 Mean :0.0255
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :1088 NA's :1105
## Day91cd8_IL2_BA.4.5.S1_resp Day91cd8_IL2_BA.4.5.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.1893 Mean :0.0119
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :1093 NA's :1094
## Day91cd8_IL2_COV2.CON.S1_resp Day91cd8_IL2_COV2.CON.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.1494 Mean :0.0115
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :1088 NA's :1088
## Day91cd8_IL2_Wuhan.N_resp Day91cd8_TNFa_BA.4.5.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.0127 Mean :0.4379
## 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :1105 NA's :1093
## Day91cd8_TNFa_BA.4.5.S2_resp Day91cd8_TNFa_COV2.CON.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.1429 Mean :0.4195
## 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :1094 NA's :1088
## Day91cd8_TNFa_COV2.CON.S2_resp Day91cd8_TNFa_Wuhan.N_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.1322 Mean :0.0064
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :1088 NA's :1105
## Day181cd4_154_BA.4.5.S1_resp Day181cd4_154_BA.4.5.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :1.0000
## Mean :0.6343 Mean :0.7252
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :1128 NA's :1131
## Day181cd4_154_COV2.CON.S1_resp Day181cd4_154_COV2.CON.S2_resp
## Min. :0.0000 Min. :0.000
## 1st Qu.:1.0000 1st Qu.:1.000
## Median :1.0000 Median :1.000
## Mean :0.7554 Mean :0.777
## 3rd Qu.:1.0000 3rd Qu.:1.000
## Max. :1.0000 Max. :1.000
## NA's :1123 NA's :1123
## Day181cd4_154_Wuhan.N_resp Day181cd4_CXCR5.154_BA.4.5.S1_resp
## Min. :0.000 Min. :0.0000
## 1st Qu.:0.000 1st Qu.:0.0000
## Median :0.000 Median :0.0000
## Mean :0.192 Mean :0.0075
## 3rd Qu.:0.000 3rd Qu.:0.0000
## Max. :1.000 Max. :1.0000
## NA's :1137 NA's :1128
## Day181cd4_CXCR5.154_BA.4.5.S2_resp Day181cd4_CXCR5.154_COV2.CON.S1_resp
## Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0
## Median :0 Median :0
## Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0
## NA's :1131 NA's :1123
## Day181cd4_CXCR5.154_COV2.CON.S2_resp Day181cd4_CXCR5.154_Wuhan.N_resp
## Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0
## Median :0 Median :0
## Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0
## NA's :1123 NA's :1143
## Day181cd4_CXCR5.IL21_BA.4.5.S1_resp Day181cd4_CXCR5.IL21_BA.4.5.S2_resp
## Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0
## Median :0 Median :0
## Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0
## NA's :1128 NA's :1131
## Day181cd4_CXCR5.IL21_COV2.CON.S1_resp Day181cd4_CXCR5.IL21_COV2.CON.S2_resp
## Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0
## Median :0 Median :0
## Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0
## NA's :1123 NA's :1123
## Day181cd4_CXCR5.IL21_Wuhan.N_resp Day181cd4_IFNg_BA.4.5.S1_resp
## Min. :0 Min. :0.0000
## 1st Qu.:0 1st Qu.:0.0000
## Median :0 Median :1.0000
## Mean :0 Mean :0.5896
## 3rd Qu.:0 3rd Qu.:1.0000
## Max. :0 Max. :1.0000
## NA's :1143 NA's :1128
## Day181cd4_IFNg_BA.4.5.S2_resp Day181cd4_IFNg_COV2.CON.S1_resp
## Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.000
## Median :1.0000 Median :1.000
## Mean :0.7328 Mean :0.705
## 3rd Qu.:1.0000 3rd Qu.:1.000
## Max. :1.0000 Max. :1.000
## NA's :1131 NA's :1123
## Day181cd4_IFNg_COV2.CON.S2_resp Day181cd4_IFNg_Wuhan.N_resp
## Min. :0.0000 Min. :0.000
## 1st Qu.:1.0000 1st Qu.:0.000
## Median :1.0000 Median :0.000
## Mean :0.7626 Mean :0.216
## 3rd Qu.:1.0000 3rd Qu.:0.000
## Max. :1.0000 Max. :1.000
## NA's :1123 NA's :1137
## Day181cd4_IFNg.IL2_BA.4.5.S1_resp Day181cd4_IFNg.IL2_BA.4.5.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :1.0000
## Mean :0.6343 Mean :0.7405
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :1128 NA's :1131
## Day181cd4_IFNg.IL2_COV2.CON.S1_resp Day181cd4_IFNg.IL2_COV2.CON.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:1.0000
## Median :1.0000 Median :1.0000
## Mean :0.6906 Mean :0.7554
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :1123 NA's :1123
## Day181cd4_IFNg.IL2_Wuhan.N_resp Day181cd4_IFNg.IL2.154_BA.4.5.S1_resp
## Min. :0.000 Min. :0.0000
## 1st Qu.:0.000 1st Qu.:0.0000
## Median :0.000 Median :1.0000
## Mean :0.224 Mean :0.6119
## 3rd Qu.:0.000 3rd Qu.:1.0000
## Max. :1.000 Max. :1.0000
## NA's :1137 NA's :1128
## Day181cd4_IFNg.IL2.154_BA.4.5.S2_resp Day181cd4_IFNg.IL2.154_COV2.CON.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :1.0000
## Mean :0.7252 Mean :0.7122
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :1131 NA's :1123
## Day181cd4_IFNg.IL2.154_COV2.CON.S2_resp Day181cd4_IFNg.IL2.154_Wuhan.N_resp
## Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.000
## Median :1.0000 Median :0.000
## Mean :0.7266 Mean :0.208
## 3rd Qu.:1.0000 3rd Qu.:0.000
## Max. :1.0000 Max. :1.000
## NA's :1123 NA's :1137
## Day181cd4_IL17a_BA.4.5.S1_resp Day181cd4_IL17a_BA.4.5.S2_resp
## Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0
## Median :0 Median :0
## Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0
## NA's :1128 NA's :1131
## Day181cd4_IL17a_COV2.CON.S1_resp Day181cd4_IL17a_COV2.CON.S2_resp
## Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0
## Median :0 Median :0
## Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0
## NA's :1123 NA's :1123
## Day181cd4_IL17a_Wuhan.N_resp Day181cd4_IL2_BA.4.5.S1_resp
## Min. :0 Min. :0.0000
## 1st Qu.:0 1st Qu.:0.0000
## Median :0 Median :0.0000
## Mean :0 Mean :0.4776
## 3rd Qu.:0 3rd Qu.:1.0000
## Max. :0 Max. :1.0000
## NA's :1137 NA's :1128
## Day181cd4_IL2_BA.4.5.S2_resp Day181cd4_IL2_COV2.CON.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :1.0000
## Mean :0.6183 Mean :0.5971
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :1131 NA's :1123
## Day181cd4_IL2_COV2.CON.S2_resp Day181cd4_IL2_Wuhan.N_resp
## Min. :0.0000 Min. :0.00
## 1st Qu.:0.0000 1st Qu.:0.00
## Median :1.0000 Median :0.00
## Mean :0.6259 Mean :0.16
## 3rd Qu.:1.0000 3rd Qu.:0.00
## Max. :1.0000 Max. :1.00
## NA's :1123 NA's :1137
## Day181cd4_IL21_BA.4.5.S1_resp Day181cd4_IL21_BA.4.5.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.0224 Mean :0.0153
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :1128 NA's :1131
## Day181cd4_IL21_COV2.CON.S1_resp Day181cd4_IL21_COV2.CON.S2_resp
## Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.000
## Median :0.0000 Median :0.000
## Mean :0.0288 Mean :0.036
## 3rd Qu.:0.0000 3rd Qu.:0.000
## Max. :1.0000 Max. :1.000
## NA's :1123 NA's :1123
## Day181cd4_IL21_Wuhan.N_resp Day181cd4_IL4.154_BA.4.5.S1_resp
## Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0
## Median :0 Median :0
## Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0
## NA's :1137 NA's :1128
## Day181cd4_IL4.154_BA.4.5.S2_resp Day181cd4_IL4.154_COV2.CON.S1_resp
## Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0
## Median :0 Median :0
## Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0
## NA's :1131 NA's :1123
## Day181cd4_IL4.154_COV2.CON.S2_resp Day181cd4_IL4.154_Wuhan.N_resp
## Min. :0.0000 Min. :0
## 1st Qu.:0.0000 1st Qu.:0
## Median :0.0000 Median :0
## Mean :0.0072 Mean :0
## 3rd Qu.:0.0000 3rd Qu.:0
## Max. :1.0000 Max. :0
## NA's :1123 NA's :1137
## Day181cd4_IL4.IL5.IL13.154_BA.4.5.S1_resp
## Min. :0
## 1st Qu.:0
## Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
## NA's :1128
## Day181cd4_IL4.IL5.IL13.154_BA.4.5.S2_resp
## Min. :0
## 1st Qu.:0
## Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
## NA's :1131
## Day181cd4_IL4.IL5.IL13.154_COV2.CON.S1_resp
## Min. :0
## 1st Qu.:0
## Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
## NA's :1123
## Day181cd4_IL4.IL5.IL13.154_COV2.CON.S2_resp
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.0144
## 3rd Qu.:0.0000
## Max. :1.0000
## NA's :1123
## Day181cd4_IL4.IL5.IL13.154_Wuhan.N_resp Day181cd4_IL5.IL13.154_BA.4.5.S1_resp
## Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0
## Median :0 Median :0
## Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0
## NA's :1137 NA's :1128
## Day181cd4_IL5.IL13.154_BA.4.5.S2_resp Day181cd4_IL5.IL13.154_COV2.CON.S1_resp
## Min. :0 Min. :0
## 1st Qu.:0 1st Qu.:0
## Median :0 Median :0
## Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0
## NA's :1131 NA's :1123
## Day181cd4_IL5.IL13.154_COV2.CON.S2_resp Day181cd4_IL5.IL13.154_Wuhan.N_resp
## Min. :0.0000 Min. :0
## 1st Qu.:0.0000 1st Qu.:0
## Median :0.0000 Median :0
## Mean :0.0072 Mean :0
## 3rd Qu.:0.0000 3rd Qu.:0
## Max. :1.0000 Max. :0
## NA's :1123 NA's :1137
## Day181cd4_TNFa_BA.4.5.S1_resp Day181cd4_TNFa_BA.4.5.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :1.0000
## Mean :0.3731 Mean :0.5038
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :1128 NA's :1131
## Day181cd4_TNFa_COV2.CON.S1_resp Day181cd4_TNFa_COV2.CON.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.3957 Mean :0.4964
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :1123 NA's :1123
## Day181cd4_TNFa_Wuhan.N_resp Day181cd8_IFNg_BA.4.5.S1_resp
## Min. :0.000 Min. :0.0000
## 1st Qu.:0.000 1st Qu.:0.0000
## Median :0.000 Median :0.0000
## Mean :0.088 Mean :0.4586
## 3rd Qu.:0.000 3rd Qu.:1.0000
## Max. :1.000 Max. :1.0000
## NA's :1137 NA's :1129
## Day181cd8_IFNg_BA.4.5.S2_resp Day181cd8_IFNg_COV2.CON.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :1.0000
## Mean :0.1679 Mean :0.5036
## 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :1131 NA's :1123
## Day181cd8_IFNg_COV2.CON.S2_resp Day181cd8_IFNg_Wuhan.N_resp
## Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.000
## Median :0.0000 Median :0.000
## Mean :0.2014 Mean :0.064
## 3rd Qu.:0.0000 3rd Qu.:0.000
## Max. :1.0000 Max. :1.000
## NA's :1123 NA's :1137
## Day181cd8_IFNg.IL2_BA.4.5.S1_resp Day181cd8_IFNg.IL2_BA.4.5.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.4511 Mean :0.1679
## 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :1129 NA's :1131
## Day181cd8_IFNg.IL2_COV2.CON.S1_resp Day181cd8_IFNg.IL2_COV2.CON.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.4892 Mean :0.1799
## 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :1123 NA's :1123
## Day181cd8_IFNg.IL2_Wuhan.N_resp Day181cd8_IFNg.IL2.TNFa_BA.4.5.S1_resp
## Min. :0.000 Min. :0.0000
## 1st Qu.:0.000 1st Qu.:0.0000
## Median :0.000 Median :0.0000
## Mean :0.064 Mean :0.4511
## 3rd Qu.:0.000 3rd Qu.:1.0000
## Max. :1.000 Max. :1.0000
## NA's :1137 NA's :1129
## Day181cd8_IFNg.IL2.TNFa_BA.4.5.S2_resp
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.1527
## 3rd Qu.:0.0000
## Max. :1.0000
## NA's :1131
## Day181cd8_IFNg.IL2.TNFa_COV2.CON.S1_resp
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.4748
## 3rd Qu.:1.0000
## Max. :1.0000
## NA's :1123
## Day181cd8_IFNg.IL2.TNFa_COV2.CON.S2_resp Day181cd8_IFNg.IL2.TNFa_Wuhan.N_resp
## Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.000
## Median :0.0000 Median :0.000
## Mean :0.1727 Mean :0.032
## 3rd Qu.:0.0000 3rd Qu.:0.000
## Max. :1.0000 Max. :1.000
## NA's :1123 NA's :1137
## Day181cd8_IL2_BA.4.5.S1_resp Day181cd8_IL2_BA.4.5.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.1278 Mean :0.0076
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :1129 NA's :1131
## Day181cd8_IL2_COV2.CON.S1_resp Day181cd8_IL2_COV2.CON.S2_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.1367 Mean :0.0072
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## NA's :1123 NA's :1123
## Day181cd8_IL2_Wuhan.N_resp Day181cd8_TNFa_BA.4.5.S1_resp
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000
## Mean :0.008 Mean :0.406
## 3rd Qu.:0.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000
## NA's :1137 NA's :1129
## Day181cd8_TNFa_BA.4.5.S2_resp Day181cd8_TNFa_COV2.CON.S1_resp
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.1298 Mean :0.4317
## 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000
## NA's :1131 NA's :1123
## Day181cd8_TNFa_COV2.CON.S2_resp Day181cd8_TNFa_Wuhan.N_resp ics
## Min. :0.0000 Min. :0.000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.0000 Median :0.000 Median :0.000
## Mean :0.1583 Mean :0.008 Mean :0.462
## 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:1.000
## Max. :1.0000 Max. :1.000 Max. :1.000
## NA's :1123 NA's :1137sort(names(dat_mapped))## [1] "Actual_visit_date_1.0"
## [2] "Actual_visit_date_15.2"
## [3] "Actual_visit_date_181.14"
## [4] "Actual_visit_date_271.14"
## [5] "Actual_visit_date_29.2"
## [6] "Actual_visit_date_91.7"
## [7] "Age"
## [8] "Age65C"
## [9] "arm"
## [10] "Asian"
## [11] "ASSAY"
## [12] "ASSCOMP_SWAB1"
## [13] "ASSCOMP_SWAB2"
## [14] "ASSQC_SWAB1"
## [15] "ASSQC_SWAB2"
## [16] "ASSSTAT_SWAB1"
## [17] "ASSSTAT_SWAB2"
## [18] "AsympInfectIndD15to181"
## [19] "AsympInfectIndD15to271"
## [20] "AsympInfectIndD15to29"
## [21] "AsympInfectIndD15to91"
## [22] "AsympInfectIndD182to271"
## [23] "AsympInfectIndD30to91"
## [24] "AsympInfectIndD92to181"
## [25] "BACRDPCT_SWAB1"
## [26] "BACRDPCT_SWAB2"
## [27] "Bcd4_154_BA.4.5.S1"
## [28] "Bcd4_154_BA.4.5.S1_resp"
## [29] "Bcd4_154_BA.4.5.S2"
## [30] "Bcd4_154_BA.4.5.S2_resp"
## [31] "Bcd4_154_COV2.CON.S1"
## [32] "Bcd4_154_COV2.CON.S1_resp"
## [33] "Bcd4_154_COV2.CON.S2"
## [34] "Bcd4_154_COV2.CON.S2_resp"
## [35] "Bcd4_154_Wuhan.N"
## [36] "Bcd4_154_Wuhan.N_resp"
## [37] "Bcd4_CXCR5.154_BA.4.5.S1"
## [38] "Bcd4_CXCR5.154_BA.4.5.S1_resp"
## [39] "Bcd4_CXCR5.154_BA.4.5.S2"
## [40] "Bcd4_CXCR5.154_BA.4.5.S2_resp"
## [41] "Bcd4_CXCR5.154_COV2.CON.S1"
## [42] "Bcd4_CXCR5.154_COV2.CON.S1_resp"
## [43] "Bcd4_CXCR5.154_COV2.CON.S2"
## [44] "Bcd4_CXCR5.154_COV2.CON.S2_resp"
## [45] "Bcd4_CXCR5.154_Wuhan.N"
## [46] "Bcd4_CXCR5.154_Wuhan.N_resp"
## [47] "Bcd4_CXCR5.IL21_BA.4.5.S1"
## [48] "Bcd4_CXCR5.IL21_BA.4.5.S1_resp"
## [49] "Bcd4_CXCR5.IL21_BA.4.5.S2"
## [50] "Bcd4_CXCR5.IL21_BA.4.5.S2_resp"
## [51] "Bcd4_CXCR5.IL21_COV2.CON.S1"
## [52] "Bcd4_CXCR5.IL21_COV2.CON.S1_resp"
## [53] "Bcd4_CXCR5.IL21_COV2.CON.S2"
## [54] "Bcd4_CXCR5.IL21_COV2.CON.S2_resp"
## [55] "Bcd4_CXCR5.IL21_Wuhan.N"
## [56] "Bcd4_CXCR5.IL21_Wuhan.N_resp"
## [57] "Bcd4_IFNg_BA.4.5.S1"
## [58] "Bcd4_IFNg_BA.4.5.S1_resp"
## [59] "Bcd4_IFNg_BA.4.5.S2"
## [60] "Bcd4_IFNg_BA.4.5.S2_resp"
## [61] "Bcd4_IFNg_COV2.CON.S1"
## [62] "Bcd4_IFNg_COV2.CON.S1_resp"
## [63] "Bcd4_IFNg_COV2.CON.S2"
## [64] "Bcd4_IFNg_COV2.CON.S2_resp"
## [65] "Bcd4_IFNg_Wuhan.N"
## [66] "Bcd4_IFNg_Wuhan.N_resp"
## [67] "Bcd4_IFNg.IL2_BA.4.5.S1"
## [68] "Bcd4_IFNg.IL2_BA.4.5.S1_resp"
## [69] "Bcd4_IFNg.IL2_BA.4.5.S2"
## [70] "Bcd4_IFNg.IL2_BA.4.5.S2_resp"
## [71] "Bcd4_IFNg.IL2_COV2.CON.S1"
## [72] "Bcd4_IFNg.IL2_COV2.CON.S1_resp"
## [73] "Bcd4_IFNg.IL2_COV2.CON.S2"
## [74] "Bcd4_IFNg.IL2_COV2.CON.S2_resp"
## [75] "Bcd4_IFNg.IL2_Wuhan.N"
## [76] "Bcd4_IFNg.IL2_Wuhan.N_resp"
## [77] "Bcd4_IFNg.IL2.154_BA.4.5.S1"
## [78] "Bcd4_IFNg.IL2.154_BA.4.5.S1_resp"
## [79] "Bcd4_IFNg.IL2.154_BA.4.5.S2"
## [80] "Bcd4_IFNg.IL2.154_BA.4.5.S2_resp"
## [81] "Bcd4_IFNg.IL2.154_COV2.CON.S1"
## [82] "Bcd4_IFNg.IL2.154_COV2.CON.S1_resp"
## [83] "Bcd4_IFNg.IL2.154_COV2.CON.S2"
## [84] "Bcd4_IFNg.IL2.154_COV2.CON.S2_resp"
## [85] "Bcd4_IFNg.IL2.154_Wuhan.N"
## [86] "Bcd4_IFNg.IL2.154_Wuhan.N_resp"
## [87] "Bcd4_IL17a_BA.4.5.S1"
## [88] "Bcd4_IL17a_BA.4.5.S1_resp"
## [89] "Bcd4_IL17a_BA.4.5.S2"
## [90] "Bcd4_IL17a_BA.4.5.S2_resp"
## [91] "Bcd4_IL17a_COV2.CON.S1"
## [92] "Bcd4_IL17a_COV2.CON.S1_resp"
## [93] "Bcd4_IL17a_COV2.CON.S2"
## [94] "Bcd4_IL17a_COV2.CON.S2_resp"
## [95] "Bcd4_IL17a_Wuhan.N"
## [96] "Bcd4_IL17a_Wuhan.N_resp"
## [97] "Bcd4_IL2_BA.4.5.S1"
## [98] "Bcd4_IL2_BA.4.5.S1_resp"
## [99] "Bcd4_IL2_BA.4.5.S2"
## [100] "Bcd4_IL2_BA.4.5.S2_resp"
## [101] "Bcd4_IL2_COV2.CON.S1"
## [102] "Bcd4_IL2_COV2.CON.S1_resp"
## [103] "Bcd4_IL2_COV2.CON.S2"
## [104] "Bcd4_IL2_COV2.CON.S2_resp"
## [105] "Bcd4_IL2_Wuhan.N"
## [106] "Bcd4_IL2_Wuhan.N_resp"
## [107] "Bcd4_IL21_BA.4.5.S1"
## [108] "Bcd4_IL21_BA.4.5.S1_resp"
## [109] "Bcd4_IL21_BA.4.5.S2"
## [110] "Bcd4_IL21_BA.4.5.S2_resp"
## [111] "Bcd4_IL21_COV2.CON.S1"
## [112] "Bcd4_IL21_COV2.CON.S1_resp"
## [113] "Bcd4_IL21_COV2.CON.S2"
## [114] "Bcd4_IL21_COV2.CON.S2_resp"
## [115] "Bcd4_IL21_Wuhan.N"
## [116] "Bcd4_IL21_Wuhan.N_resp"
## [117] "Bcd4_IL4.154_BA.4.5.S1"
## [118] "Bcd4_IL4.154_BA.4.5.S1_resp"
## [119] "Bcd4_IL4.154_BA.4.5.S2"
## [120] "Bcd4_IL4.154_BA.4.5.S2_resp"
## [121] "Bcd4_IL4.154_COV2.CON.S1"
## [122] "Bcd4_IL4.154_COV2.CON.S1_resp"
## [123] "Bcd4_IL4.154_COV2.CON.S2"
## [124] "Bcd4_IL4.154_COV2.CON.S2_resp"
## [125] "Bcd4_IL4.154_Wuhan.N"
## [126] "Bcd4_IL4.154_Wuhan.N_resp"
## [127] "Bcd4_IL4.IL5.IL13.154_BA.4.5.S1"
## [128] "Bcd4_IL4.IL5.IL13.154_BA.4.5.S1_resp"
## [129] "Bcd4_IL4.IL5.IL13.154_BA.4.5.S2"
## [130] "Bcd4_IL4.IL5.IL13.154_BA.4.5.S2_resp"
## [131] "Bcd4_IL4.IL5.IL13.154_COV2.CON.S1"
## [132] "Bcd4_IL4.IL5.IL13.154_COV2.CON.S1_resp"
## [133] "Bcd4_IL4.IL5.IL13.154_COV2.CON.S2"
## [134] "Bcd4_IL4.IL5.IL13.154_COV2.CON.S2_resp"
## [135] "Bcd4_IL4.IL5.IL13.154_Wuhan.N"
## [136] "Bcd4_IL4.IL5.IL13.154_Wuhan.N_resp"
## [137] "Bcd4_IL5.IL13.154_BA.4.5.S1"
## [138] "Bcd4_IL5.IL13.154_BA.4.5.S1_resp"
## [139] "Bcd4_IL5.IL13.154_BA.4.5.S2"
## [140] "Bcd4_IL5.IL13.154_BA.4.5.S2_resp"
## [141] "Bcd4_IL5.IL13.154_COV2.CON.S1"
## [142] "Bcd4_IL5.IL13.154_COV2.CON.S1_resp"
## [143] "Bcd4_IL5.IL13.154_COV2.CON.S2"
## [144] "Bcd4_IL5.IL13.154_COV2.CON.S2_resp"
## [145] "Bcd4_IL5.IL13.154_Wuhan.N"
## [146] "Bcd4_IL5.IL13.154_Wuhan.N_resp"
## [147] "Bcd4_TNFa_BA.4.5.S1"
## [148] "Bcd4_TNFa_BA.4.5.S1_resp"
## [149] "Bcd4_TNFa_BA.4.5.S2"
## [150] "Bcd4_TNFa_BA.4.5.S2_resp"
## [151] "Bcd4_TNFa_COV2.CON.S1"
## [152] "Bcd4_TNFa_COV2.CON.S1_resp"
## [153] "Bcd4_TNFa_COV2.CON.S2"
## [154] "Bcd4_TNFa_COV2.CON.S2_resp"
## [155] "Bcd4_TNFa_Wuhan.N"
## [156] "Bcd4_TNFa_Wuhan.N_resp"
## [157] "Bcd8_IFNg_BA.4.5.S1"
## [158] "Bcd8_IFNg_BA.4.5.S1_resp"
## [159] "Bcd8_IFNg_BA.4.5.S2"
## [160] "Bcd8_IFNg_BA.4.5.S2_resp"
## [161] "Bcd8_IFNg_COV2.CON.S1"
## [162] "Bcd8_IFNg_COV2.CON.S1_resp"
## [163] "Bcd8_IFNg_COV2.CON.S2"
## [164] "Bcd8_IFNg_COV2.CON.S2_resp"
## [165] "Bcd8_IFNg_Wuhan.N"
## [166] "Bcd8_IFNg_Wuhan.N_resp"
## [167] "Bcd8_IFNg.IL2_BA.4.5.S1"
## [168] "Bcd8_IFNg.IL2_BA.4.5.S1_resp"
## [169] "Bcd8_IFNg.IL2_BA.4.5.S2"
## [170] "Bcd8_IFNg.IL2_BA.4.5.S2_resp"
## [171] "Bcd8_IFNg.IL2_COV2.CON.S1"
## [172] "Bcd8_IFNg.IL2_COV2.CON.S1_resp"
## [173] "Bcd8_IFNg.IL2_COV2.CON.S2"
## [174] "Bcd8_IFNg.IL2_COV2.CON.S2_resp"
## [175] "Bcd8_IFNg.IL2_Wuhan.N"
## [176] "Bcd8_IFNg.IL2_Wuhan.N_resp"
## [177] "Bcd8_IFNg.IL2.TNFa_BA.4.5.S1"
## [178] "Bcd8_IFNg.IL2.TNFa_BA.4.5.S1_resp"
## [179] "Bcd8_IFNg.IL2.TNFa_BA.4.5.S2"
## [180] "Bcd8_IFNg.IL2.TNFa_BA.4.5.S2_resp"
## [181] "Bcd8_IFNg.IL2.TNFa_COV2.CON.S1"
## [182] "Bcd8_IFNg.IL2.TNFa_COV2.CON.S1_resp"
## [183] "Bcd8_IFNg.IL2.TNFa_COV2.CON.S2"
## [184] "Bcd8_IFNg.IL2.TNFa_COV2.CON.S2_resp"
## [185] "Bcd8_IFNg.IL2.TNFa_Wuhan.N"
## [186] "Bcd8_IFNg.IL2.TNFa_Wuhan.N_resp"
## [187] "Bcd8_IL2_BA.4.5.S1"
## [188] "Bcd8_IL2_BA.4.5.S1_resp"
## [189] "Bcd8_IL2_BA.4.5.S2"
## [190] "Bcd8_IL2_BA.4.5.S2_resp"
## [191] "Bcd8_IL2_COV2.CON.S1"
## [192] "Bcd8_IL2_COV2.CON.S1_resp"
## [193] "Bcd8_IL2_COV2.CON.S2"
## [194] "Bcd8_IL2_COV2.CON.S2_resp"
## [195] "Bcd8_IL2_Wuhan.N"
## [196] "Bcd8_IL2_Wuhan.N_resp"
## [197] "Bcd8_TNFa_BA.4.5.S1"
## [198] "Bcd8_TNFa_BA.4.5.S1_resp"
## [199] "Bcd8_TNFa_BA.4.5.S2"
## [200] "Bcd8_TNFa_BA.4.5.S2_resp"
## [201] "Bcd8_TNFa_COV2.CON.S1"
## [202] "Bcd8_TNFa_COV2.CON.S1_resp"
## [203] "Bcd8_TNFa_COV2.CON.S2"
## [204] "Bcd8_TNFa_COV2.CON.S2_resp"
## [205] "Bcd8_TNFa_Wuhan.N"
## [206] "Bcd8_TNFa_Wuhan.N_resp"
## [207] "Black"
## [208] "Bpseudoneutid50_BA.1"
## [209] "Bpseudoneutid50_BA.4.BA.5"
## [210] "Bpseudoneutid50_Beta"
## [211] "Bpseudoneutid50_D614G"
## [212] "Bpseudoneutid50_Delta"
## [213] "Bpseudoneutid50Duke_BA.2.12.1"
## [214] "City"
## [215] "CNSR_D22toD181"
## [216] "CNSR_D22toD91"
## [217] "CNSR_D22toend"
## [218] "CNSR_D36toD181"
## [219] "CNSR_D92toD181"
## [220] "CNSR1_D22toD181"
## [221] "CNSR1_D22toD91"
## [222] "CNSR1_D22toend"
## [223] "CNSR1_D36toD181"
## [224] "CNSR1_D92toD181"
## [225] "CNSR2"
## [226] "CNSR3"
## [227] "CNSR4"
## [228] "CNSR5"
## [229] "COVIDIndD22toD181"
## [230] "COVIDIndD22toD91"
## [231] "COVIDIndD22toend"
## [232] "COVIDIndD36toD181"
## [233] "COVIDIndD92toD181"
## [234] "COVIDtimeD22toD181"
## [235] "COVIDtimeD22toD91"
## [236] "COVIDtimeD36toD181"
## [237] "COVIDtimeD92toD181"
## [238] "COVRDPCT_SWAB1"
## [239] "COVRDPCT_SWAB2"
## [240] "CTSTAT_SWAB1"
## [241] "CTSTAT_SWAB2"
## [242] "CTVALUE_SWAB1"
## [243] "CTVALUE_SWAB2"
## [244] "D1_D15_flag"
## [245] "D1_D29_flag"
## [246] "DATE_COLLECT_SWAB1"
## [247] "DATE_COLLECT_SWAB2"
## [248] "Day147pseudoneutid50_BA.1"
## [249] "Day147pseudoneutid50_BA.4.BA.5"
## [250] "Day147pseudoneutid50_Beta"
## [251] "Day147pseudoneutid50_D614G"
## [252] "Day147pseudoneutid50_Delta"
## [253] "Day15cd4_154_BA.4.5.S1"
## [254] "Day15cd4_154_BA.4.5.S1_resp"
## [255] "Day15cd4_154_BA.4.5.S2"
## [256] "Day15cd4_154_BA.4.5.S2_resp"
## [257] "Day15cd4_154_COV2.CON.S1"
## [258] "Day15cd4_154_COV2.CON.S1_resp"
## [259] "Day15cd4_154_COV2.CON.S2"
## [260] "Day15cd4_154_COV2.CON.S2_resp"
## [261] "Day15cd4_154_Wuhan.N"
## [262] "Day15cd4_154_Wuhan.N_resp"
## [263] "Day15cd4_CXCR5.154_BA.4.5.S1"
## [264] "Day15cd4_CXCR5.154_BA.4.5.S1_resp"
## [265] "Day15cd4_CXCR5.154_BA.4.5.S2"
## [266] "Day15cd4_CXCR5.154_BA.4.5.S2_resp"
## [267] "Day15cd4_CXCR5.154_COV2.CON.S1"
## [268] "Day15cd4_CXCR5.154_COV2.CON.S1_resp"
## [269] "Day15cd4_CXCR5.154_COV2.CON.S2"
## [270] "Day15cd4_CXCR5.154_COV2.CON.S2_resp"
## [271] "Day15cd4_CXCR5.154_Wuhan.N"
## [272] "Day15cd4_CXCR5.154_Wuhan.N_resp"
## [273] "Day15cd4_CXCR5.IL21_BA.4.5.S1"
## [274] "Day15cd4_CXCR5.IL21_BA.4.5.S1_resp"
## [275] "Day15cd4_CXCR5.IL21_BA.4.5.S2"
## [276] "Day15cd4_CXCR5.IL21_BA.4.5.S2_resp"
## [277] "Day15cd4_CXCR5.IL21_COV2.CON.S1"
## [278] "Day15cd4_CXCR5.IL21_COV2.CON.S1_resp"
## [279] "Day15cd4_CXCR5.IL21_COV2.CON.S2"
## [280] "Day15cd4_CXCR5.IL21_COV2.CON.S2_resp"
## [281] "Day15cd4_CXCR5.IL21_Wuhan.N"
## [282] "Day15cd4_CXCR5.IL21_Wuhan.N_resp"
## [283] "Day15cd4_IFNg_BA.4.5.S1"
## [284] "Day15cd4_IFNg_BA.4.5.S1_resp"
## [285] "Day15cd4_IFNg_BA.4.5.S2"
## [286] "Day15cd4_IFNg_BA.4.5.S2_resp"
## [287] "Day15cd4_IFNg_COV2.CON.S1"
## [288] "Day15cd4_IFNg_COV2.CON.S1_resp"
## [289] "Day15cd4_IFNg_COV2.CON.S2"
## [290] "Day15cd4_IFNg_COV2.CON.S2_resp"
## [291] "Day15cd4_IFNg_Wuhan.N"
## [292] "Day15cd4_IFNg_Wuhan.N_resp"
## [293] "Day15cd4_IFNg.IL2_BA.4.5.S1"
## [294] "Day15cd4_IFNg.IL2_BA.4.5.S1_resp"
## [295] "Day15cd4_IFNg.IL2_BA.4.5.S2"
## [296] "Day15cd4_IFNg.IL2_BA.4.5.S2_resp"
## [297] "Day15cd4_IFNg.IL2_COV2.CON.S1"
## [298] "Day15cd4_IFNg.IL2_COV2.CON.S1_resp"
## [299] "Day15cd4_IFNg.IL2_COV2.CON.S2"
## [300] "Day15cd4_IFNg.IL2_COV2.CON.S2_resp"
## [301] "Day15cd4_IFNg.IL2_Wuhan.N"
## [302] "Day15cd4_IFNg.IL2_Wuhan.N_resp"
## [303] "Day15cd4_IFNg.IL2.154_BA.4.5.S1"
## [304] "Day15cd4_IFNg.IL2.154_BA.4.5.S1_resp"
## [305] "Day15cd4_IFNg.IL2.154_BA.4.5.S2"
## [306] "Day15cd4_IFNg.IL2.154_BA.4.5.S2_resp"
## [307] "Day15cd4_IFNg.IL2.154_COV2.CON.S1"
## [308] "Day15cd4_IFNg.IL2.154_COV2.CON.S1_resp"
## [309] "Day15cd4_IFNg.IL2.154_COV2.CON.S2"
## [310] "Day15cd4_IFNg.IL2.154_COV2.CON.S2_resp"
## [311] "Day15cd4_IFNg.IL2.154_Wuhan.N"
## [312] "Day15cd4_IFNg.IL2.154_Wuhan.N_resp"
## [313] "Day15cd4_IL17a_BA.4.5.S1"
## [314] "Day15cd4_IL17a_BA.4.5.S1_resp"
## [315] "Day15cd4_IL17a_BA.4.5.S2"
## [316] "Day15cd4_IL17a_BA.4.5.S2_resp"
## [317] "Day15cd4_IL17a_COV2.CON.S1"
## [318] "Day15cd4_IL17a_COV2.CON.S1_resp"
## [319] "Day15cd4_IL17a_COV2.CON.S2"
## [320] "Day15cd4_IL17a_COV2.CON.S2_resp"
## [321] "Day15cd4_IL17a_Wuhan.N"
## [322] "Day15cd4_IL17a_Wuhan.N_resp"
## [323] "Day15cd4_IL2_BA.4.5.S1"
## [324] "Day15cd4_IL2_BA.4.5.S1_resp"
## [325] "Day15cd4_IL2_BA.4.5.S2"
## [326] "Day15cd4_IL2_BA.4.5.S2_resp"
## [327] "Day15cd4_IL2_COV2.CON.S1"
## [328] "Day15cd4_IL2_COV2.CON.S1_resp"
## [329] "Day15cd4_IL2_COV2.CON.S2"
## [330] "Day15cd4_IL2_COV2.CON.S2_resp"
## [331] "Day15cd4_IL2_Wuhan.N"
## [332] "Day15cd4_IL2_Wuhan.N_resp"
## [333] "Day15cd4_IL21_BA.4.5.S1"
## [334] "Day15cd4_IL21_BA.4.5.S1_resp"
## [335] "Day15cd4_IL21_BA.4.5.S2"
## [336] "Day15cd4_IL21_BA.4.5.S2_resp"
## [337] "Day15cd4_IL21_COV2.CON.S1"
## [338] "Day15cd4_IL21_COV2.CON.S1_resp"
## [339] "Day15cd4_IL21_COV2.CON.S2"
## [340] "Day15cd4_IL21_COV2.CON.S2_resp"
## [341] "Day15cd4_IL21_Wuhan.N"
## [342] "Day15cd4_IL21_Wuhan.N_resp"
## [343] "Day15cd4_IL4.154_BA.4.5.S1"
## [344] "Day15cd4_IL4.154_BA.4.5.S1_resp"
## [345] "Day15cd4_IL4.154_BA.4.5.S2"
## [346] "Day15cd4_IL4.154_BA.4.5.S2_resp"
## [347] "Day15cd4_IL4.154_COV2.CON.S1"
## [348] "Day15cd4_IL4.154_COV2.CON.S1_resp"
## [349] "Day15cd4_IL4.154_COV2.CON.S2"
## [350] "Day15cd4_IL4.154_COV2.CON.S2_resp"
## [351] "Day15cd4_IL4.154_Wuhan.N"
## [352] "Day15cd4_IL4.154_Wuhan.N_resp"
## [353] "Day15cd4_IL4.IL5.IL13.154_BA.4.5.S1"
## [354] "Day15cd4_IL4.IL5.IL13.154_BA.4.5.S1_resp"
## [355] "Day15cd4_IL4.IL5.IL13.154_BA.4.5.S2"
## [356] "Day15cd4_IL4.IL5.IL13.154_BA.4.5.S2_resp"
## [357] "Day15cd4_IL4.IL5.IL13.154_COV2.CON.S1"
## [358] "Day15cd4_IL4.IL5.IL13.154_COV2.CON.S1_resp"
## [359] "Day15cd4_IL4.IL5.IL13.154_COV2.CON.S2"
## [360] "Day15cd4_IL4.IL5.IL13.154_COV2.CON.S2_resp"
## [361] "Day15cd4_IL4.IL5.IL13.154_Wuhan.N"
## [362] "Day15cd4_IL4.IL5.IL13.154_Wuhan.N_resp"
## [363] "Day15cd4_IL5.IL13.154_BA.4.5.S1"
## [364] "Day15cd4_IL5.IL13.154_BA.4.5.S1_resp"
## [365] "Day15cd4_IL5.IL13.154_BA.4.5.S2"
## [366] "Day15cd4_IL5.IL13.154_BA.4.5.S2_resp"
## [367] "Day15cd4_IL5.IL13.154_COV2.CON.S1"
## [368] "Day15cd4_IL5.IL13.154_COV2.CON.S1_resp"
## [369] "Day15cd4_IL5.IL13.154_COV2.CON.S2"
## [370] "Day15cd4_IL5.IL13.154_COV2.CON.S2_resp"
## [371] "Day15cd4_IL5.IL13.154_Wuhan.N"
## [372] "Day15cd4_IL5.IL13.154_Wuhan.N_resp"
## [373] "Day15cd4_TNFa_BA.4.5.S1"
## [374] "Day15cd4_TNFa_BA.4.5.S1_resp"
## [375] "Day15cd4_TNFa_BA.4.5.S2"
## [376] "Day15cd4_TNFa_BA.4.5.S2_resp"
## [377] "Day15cd4_TNFa_COV2.CON.S1"
## [378] "Day15cd4_TNFa_COV2.CON.S1_resp"
## [379] "Day15cd4_TNFa_COV2.CON.S2"
## [380] "Day15cd4_TNFa_COV2.CON.S2_resp"
## [381] "Day15cd4_TNFa_Wuhan.N"
## [382] "Day15cd4_TNFa_Wuhan.N_resp"
## [383] "Day15cd8_IFNg_BA.4.5.S1"
## [384] "Day15cd8_IFNg_BA.4.5.S1_resp"
## [385] "Day15cd8_IFNg_BA.4.5.S2"
## [386] "Day15cd8_IFNg_BA.4.5.S2_resp"
## [387] "Day15cd8_IFNg_COV2.CON.S1"
## [388] "Day15cd8_IFNg_COV2.CON.S1_resp"
## [389] "Day15cd8_IFNg_COV2.CON.S2"
## [390] "Day15cd8_IFNg_COV2.CON.S2_resp"
## [391] "Day15cd8_IFNg_Wuhan.N"
## [392] "Day15cd8_IFNg_Wuhan.N_resp"
## [393] "Day15cd8_IFNg.IL2_BA.4.5.S1"
## [394] "Day15cd8_IFNg.IL2_BA.4.5.S1_resp"
## [395] "Day15cd8_IFNg.IL2_BA.4.5.S2"
## [396] "Day15cd8_IFNg.IL2_BA.4.5.S2_resp"
## [397] "Day15cd8_IFNg.IL2_COV2.CON.S1"
## [398] "Day15cd8_IFNg.IL2_COV2.CON.S1_resp"
## [399] "Day15cd8_IFNg.IL2_COV2.CON.S2"
## [400] "Day15cd8_IFNg.IL2_COV2.CON.S2_resp"
## [401] "Day15cd8_IFNg.IL2_Wuhan.N"
## [402] "Day15cd8_IFNg.IL2_Wuhan.N_resp"
## [403] "Day15cd8_IFNg.IL2.TNFa_BA.4.5.S1"
## [404] "Day15cd8_IFNg.IL2.TNFa_BA.4.5.S1_resp"
## [405] "Day15cd8_IFNg.IL2.TNFa_BA.4.5.S2"
## [406] "Day15cd8_IFNg.IL2.TNFa_BA.4.5.S2_resp"
## [407] "Day15cd8_IFNg.IL2.TNFa_COV2.CON.S1"
## [408] "Day15cd8_IFNg.IL2.TNFa_COV2.CON.S1_resp"
## [409] "Day15cd8_IFNg.IL2.TNFa_COV2.CON.S2"
## [410] "Day15cd8_IFNg.IL2.TNFa_COV2.CON.S2_resp"
## [411] "Day15cd8_IFNg.IL2.TNFa_Wuhan.N"
## [412] "Day15cd8_IFNg.IL2.TNFa_Wuhan.N_resp"
## [413] "Day15cd8_IL2_BA.4.5.S1"
## [414] "Day15cd8_IL2_BA.4.5.S1_resp"
## [415] "Day15cd8_IL2_BA.4.5.S2"
## [416] "Day15cd8_IL2_BA.4.5.S2_resp"
## [417] "Day15cd8_IL2_COV2.CON.S1"
## [418] "Day15cd8_IL2_COV2.CON.S1_resp"
## [419] "Day15cd8_IL2_COV2.CON.S2"
## [420] "Day15cd8_IL2_COV2.CON.S2_resp"
## [421] "Day15cd8_IL2_Wuhan.N"
## [422] "Day15cd8_IL2_Wuhan.N_resp"
## [423] "Day15cd8_TNFa_BA.4.5.S1"
## [424] "Day15cd8_TNFa_BA.4.5.S1_resp"
## [425] "Day15cd8_TNFa_BA.4.5.S2"
## [426] "Day15cd8_TNFa_BA.4.5.S2_resp"
## [427] "Day15cd8_TNFa_COV2.CON.S1"
## [428] "Day15cd8_TNFa_COV2.CON.S1_resp"
## [429] "Day15cd8_TNFa_COV2.CON.S2"
## [430] "Day15cd8_TNFa_COV2.CON.S2_resp"
## [431] "Day15cd8_TNFa_Wuhan.N"
## [432] "Day15cd8_TNFa_Wuhan.N_resp"
## [433] "Day15pseudoneutid50_BA.1"
## [434] "Day15pseudoneutid50_BA.4.BA.5"
## [435] "Day15pseudoneutid50_Beta"
## [436] "Day15pseudoneutid50_D614G"
## [437] "Day15pseudoneutid50_Delta"
## [438] "Day15pseudoneutid50Duke_BA.2.12.1"
## [439] "Day181cd4_154_BA.4.5.S1"
## [440] "Day181cd4_154_BA.4.5.S1_resp"
## [441] "Day181cd4_154_BA.4.5.S2"
## [442] "Day181cd4_154_BA.4.5.S2_resp"
## [443] "Day181cd4_154_COV2.CON.S1"
## [444] "Day181cd4_154_COV2.CON.S1_resp"
## [445] "Day181cd4_154_COV2.CON.S2"
## [446] "Day181cd4_154_COV2.CON.S2_resp"
## [447] "Day181cd4_154_Wuhan.N"
## [448] "Day181cd4_154_Wuhan.N_resp"
## [449] "Day181cd4_CXCR5.154_BA.4.5.S1"
## [450] "Day181cd4_CXCR5.154_BA.4.5.S1_resp"
## [451] "Day181cd4_CXCR5.154_BA.4.5.S2"
## [452] "Day181cd4_CXCR5.154_BA.4.5.S2_resp"
## [453] "Day181cd4_CXCR5.154_COV2.CON.S1"
## [454] "Day181cd4_CXCR5.154_COV2.CON.S1_resp"
## [455] "Day181cd4_CXCR5.154_COV2.CON.S2"
## [456] "Day181cd4_CXCR5.154_COV2.CON.S2_resp"
## [457] "Day181cd4_CXCR5.154_Wuhan.N"
## [458] "Day181cd4_CXCR5.154_Wuhan.N_resp"
## [459] "Day181cd4_CXCR5.IL21_BA.4.5.S1"
## [460] "Day181cd4_CXCR5.IL21_BA.4.5.S1_resp"
## [461] "Day181cd4_CXCR5.IL21_BA.4.5.S2"
## [462] "Day181cd4_CXCR5.IL21_BA.4.5.S2_resp"
## [463] "Day181cd4_CXCR5.IL21_COV2.CON.S1"
## [464] "Day181cd4_CXCR5.IL21_COV2.CON.S1_resp"
## [465] "Day181cd4_CXCR5.IL21_COV2.CON.S2"
## [466] "Day181cd4_CXCR5.IL21_COV2.CON.S2_resp"
## [467] "Day181cd4_CXCR5.IL21_Wuhan.N"
## [468] "Day181cd4_CXCR5.IL21_Wuhan.N_resp"
## [469] "Day181cd4_IFNg_BA.4.5.S1"
## [470] "Day181cd4_IFNg_BA.4.5.S1_resp"
## [471] "Day181cd4_IFNg_BA.4.5.S2"
## [472] "Day181cd4_IFNg_BA.4.5.S2_resp"
## [473] "Day181cd4_IFNg_COV2.CON.S1"
## [474] "Day181cd4_IFNg_COV2.CON.S1_resp"
## [475] "Day181cd4_IFNg_COV2.CON.S2"
## [476] "Day181cd4_IFNg_COV2.CON.S2_resp"
## [477] "Day181cd4_IFNg_Wuhan.N"
## [478] "Day181cd4_IFNg_Wuhan.N_resp"
## [479] "Day181cd4_IFNg.IL2_BA.4.5.S1"
## [480] "Day181cd4_IFNg.IL2_BA.4.5.S1_resp"
## [481] "Day181cd4_IFNg.IL2_BA.4.5.S2"
## [482] "Day181cd4_IFNg.IL2_BA.4.5.S2_resp"
## [483] "Day181cd4_IFNg.IL2_COV2.CON.S1"
## [484] "Day181cd4_IFNg.IL2_COV2.CON.S1_resp"
## [485] "Day181cd4_IFNg.IL2_COV2.CON.S2"
## [486] "Day181cd4_IFNg.IL2_COV2.CON.S2_resp"
## [487] "Day181cd4_IFNg.IL2_Wuhan.N"
## [488] "Day181cd4_IFNg.IL2_Wuhan.N_resp"
## [489] "Day181cd4_IFNg.IL2.154_BA.4.5.S1"
## [490] "Day181cd4_IFNg.IL2.154_BA.4.5.S1_resp"
## [491] "Day181cd4_IFNg.IL2.154_BA.4.5.S2"
## [492] "Day181cd4_IFNg.IL2.154_BA.4.5.S2_resp"
## [493] "Day181cd4_IFNg.IL2.154_COV2.CON.S1"
## [494] "Day181cd4_IFNg.IL2.154_COV2.CON.S1_resp"
## [495] "Day181cd4_IFNg.IL2.154_COV2.CON.S2"
## [496] "Day181cd4_IFNg.IL2.154_COV2.CON.S2_resp"
## [497] "Day181cd4_IFNg.IL2.154_Wuhan.N"
## [498] "Day181cd4_IFNg.IL2.154_Wuhan.N_resp"
## [499] "Day181cd4_IL17a_BA.4.5.S1"
## [500] "Day181cd4_IL17a_BA.4.5.S1_resp"
## [501] "Day181cd4_IL17a_BA.4.5.S2"
## [502] "Day181cd4_IL17a_BA.4.5.S2_resp"
## [503] "Day181cd4_IL17a_COV2.CON.S1"
## [504] "Day181cd4_IL17a_COV2.CON.S1_resp"
## [505] "Day181cd4_IL17a_COV2.CON.S2"
## [506] "Day181cd4_IL17a_COV2.CON.S2_resp"
## [507] "Day181cd4_IL17a_Wuhan.N"
## [508] "Day181cd4_IL17a_Wuhan.N_resp"
## [509] "Day181cd4_IL2_BA.4.5.S1"
## [510] "Day181cd4_IL2_BA.4.5.S1_resp"
## [511] "Day181cd4_IL2_BA.4.5.S2"
## [512] "Day181cd4_IL2_BA.4.5.S2_resp"
## [513] "Day181cd4_IL2_COV2.CON.S1"
## [514] "Day181cd4_IL2_COV2.CON.S1_resp"
## [515] "Day181cd4_IL2_COV2.CON.S2"
## [516] "Day181cd4_IL2_COV2.CON.S2_resp"
## [517] "Day181cd4_IL2_Wuhan.N"
## [518] "Day181cd4_IL2_Wuhan.N_resp"
## [519] "Day181cd4_IL21_BA.4.5.S1"
## [520] "Day181cd4_IL21_BA.4.5.S1_resp"
## [521] "Day181cd4_IL21_BA.4.5.S2"
## [522] "Day181cd4_IL21_BA.4.5.S2_resp"
## [523] "Day181cd4_IL21_COV2.CON.S1"
## [524] "Day181cd4_IL21_COV2.CON.S1_resp"
## [525] "Day181cd4_IL21_COV2.CON.S2"
## [526] "Day181cd4_IL21_COV2.CON.S2_resp"
## [527] "Day181cd4_IL21_Wuhan.N"
## [528] "Day181cd4_IL21_Wuhan.N_resp"
## [529] "Day181cd4_IL4.154_BA.4.5.S1"
## [530] "Day181cd4_IL4.154_BA.4.5.S1_resp"
## [531] "Day181cd4_IL4.154_BA.4.5.S2"
## [532] "Day181cd4_IL4.154_BA.4.5.S2_resp"
## [533] "Day181cd4_IL4.154_COV2.CON.S1"
## [534] "Day181cd4_IL4.154_COV2.CON.S1_resp"
## [535] "Day181cd4_IL4.154_COV2.CON.S2"
## [536] "Day181cd4_IL4.154_COV2.CON.S2_resp"
## [537] "Day181cd4_IL4.154_Wuhan.N"
## [538] "Day181cd4_IL4.154_Wuhan.N_resp"
## [539] "Day181cd4_IL4.IL5.IL13.154_BA.4.5.S1"
## [540] "Day181cd4_IL4.IL5.IL13.154_BA.4.5.S1_resp"
## [541] "Day181cd4_IL4.IL5.IL13.154_BA.4.5.S2"
## [542] "Day181cd4_IL4.IL5.IL13.154_BA.4.5.S2_resp"
## [543] "Day181cd4_IL4.IL5.IL13.154_COV2.CON.S1"
## [544] "Day181cd4_IL4.IL5.IL13.154_COV2.CON.S1_resp"
## [545] "Day181cd4_IL4.IL5.IL13.154_COV2.CON.S2"
## [546] "Day181cd4_IL4.IL5.IL13.154_COV2.CON.S2_resp"
## [547] "Day181cd4_IL4.IL5.IL13.154_Wuhan.N"
## [548] "Day181cd4_IL4.IL5.IL13.154_Wuhan.N_resp"
## [549] "Day181cd4_IL5.IL13.154_BA.4.5.S1"
## [550] "Day181cd4_IL5.IL13.154_BA.4.5.S1_resp"
## [551] "Day181cd4_IL5.IL13.154_BA.4.5.S2"
## [552] "Day181cd4_IL5.IL13.154_BA.4.5.S2_resp"
## [553] "Day181cd4_IL5.IL13.154_COV2.CON.S1"
## [554] "Day181cd4_IL5.IL13.154_COV2.CON.S1_resp"
## [555] "Day181cd4_IL5.IL13.154_COV2.CON.S2"
## [556] "Day181cd4_IL5.IL13.154_COV2.CON.S2_resp"
## [557] "Day181cd4_IL5.IL13.154_Wuhan.N"
## [558] "Day181cd4_IL5.IL13.154_Wuhan.N_resp"
## [559] "Day181cd4_TNFa_BA.4.5.S1"
## [560] "Day181cd4_TNFa_BA.4.5.S1_resp"
## [561] "Day181cd4_TNFa_BA.4.5.S2"
## [562] "Day181cd4_TNFa_BA.4.5.S2_resp"
## [563] "Day181cd4_TNFa_COV2.CON.S1"
## [564] "Day181cd4_TNFa_COV2.CON.S1_resp"
## [565] "Day181cd4_TNFa_COV2.CON.S2"
## [566] "Day181cd4_TNFa_COV2.CON.S2_resp"
## [567] "Day181cd4_TNFa_Wuhan.N"
## [568] "Day181cd4_TNFa_Wuhan.N_resp"
## [569] "Day181cd8_IFNg_BA.4.5.S1"
## [570] "Day181cd8_IFNg_BA.4.5.S1_resp"
## [571] "Day181cd8_IFNg_BA.4.5.S2"
## [572] "Day181cd8_IFNg_BA.4.5.S2_resp"
## [573] "Day181cd8_IFNg_COV2.CON.S1"
## [574] "Day181cd8_IFNg_COV2.CON.S1_resp"
## [575] "Day181cd8_IFNg_COV2.CON.S2"
## [576] "Day181cd8_IFNg_COV2.CON.S2_resp"
## [577] "Day181cd8_IFNg_Wuhan.N"
## [578] "Day181cd8_IFNg_Wuhan.N_resp"
## [579] "Day181cd8_IFNg.IL2_BA.4.5.S1"
## [580] "Day181cd8_IFNg.IL2_BA.4.5.S1_resp"
## [581] "Day181cd8_IFNg.IL2_BA.4.5.S2"
## [582] "Day181cd8_IFNg.IL2_BA.4.5.S2_resp"
## [583] "Day181cd8_IFNg.IL2_COV2.CON.S1"
## [584] "Day181cd8_IFNg.IL2_COV2.CON.S1_resp"
## [585] "Day181cd8_IFNg.IL2_COV2.CON.S2"
## [586] "Day181cd8_IFNg.IL2_COV2.CON.S2_resp"
## [587] "Day181cd8_IFNg.IL2_Wuhan.N"
## [588] "Day181cd8_IFNg.IL2_Wuhan.N_resp"
## [589] "Day181cd8_IFNg.IL2.TNFa_BA.4.5.S1"
## [590] "Day181cd8_IFNg.IL2.TNFa_BA.4.5.S1_resp"
## [591] "Day181cd8_IFNg.IL2.TNFa_BA.4.5.S2"
## [592] "Day181cd8_IFNg.IL2.TNFa_BA.4.5.S2_resp"
## [593] "Day181cd8_IFNg.IL2.TNFa_COV2.CON.S1"
## [594] "Day181cd8_IFNg.IL2.TNFa_COV2.CON.S1_resp"
## [595] "Day181cd8_IFNg.IL2.TNFa_COV2.CON.S2"
## [596] "Day181cd8_IFNg.IL2.TNFa_COV2.CON.S2_resp"
## [597] "Day181cd8_IFNg.IL2.TNFa_Wuhan.N"
## [598] "Day181cd8_IFNg.IL2.TNFa_Wuhan.N_resp"
## [599] "Day181cd8_IL2_BA.4.5.S1"
## [600] "Day181cd8_IL2_BA.4.5.S1_resp"
## [601] "Day181cd8_IL2_BA.4.5.S2"
## [602] "Day181cd8_IL2_BA.4.5.S2_resp"
## [603] "Day181cd8_IL2_COV2.CON.S1"
## [604] "Day181cd8_IL2_COV2.CON.S1_resp"
## [605] "Day181cd8_IL2_COV2.CON.S2"
## [606] "Day181cd8_IL2_COV2.CON.S2_resp"
## [607] "Day181cd8_IL2_Wuhan.N"
## [608] "Day181cd8_IL2_Wuhan.N_resp"
## [609] "Day181cd8_TNFa_BA.4.5.S1"
## [610] "Day181cd8_TNFa_BA.4.5.S1_resp"
## [611] "Day181cd8_TNFa_BA.4.5.S2"
## [612] "Day181cd8_TNFa_BA.4.5.S2_resp"
## [613] "Day181cd8_TNFa_COV2.CON.S1"
## [614] "Day181cd8_TNFa_COV2.CON.S1_resp"
## [615] "Day181cd8_TNFa_COV2.CON.S2"
## [616] "Day181cd8_TNFa_COV2.CON.S2_resp"
## [617] "Day181cd8_TNFa_Wuhan.N"
## [618] "Day181cd8_TNFa_Wuhan.N_resp"
## [619] "Day181pseudoneutid50_BA.1"
## [620] "Day181pseudoneutid50_BA.4.BA.5"
## [621] "Day181pseudoneutid50_Beta"
## [622] "Day181pseudoneutid50_D614G"
## [623] "Day181pseudoneutid50_Delta"
## [624] "Day29pseudoneutid50_BA.1"
## [625] "Day29pseudoneutid50_BA.4.BA.5"
## [626] "Day29pseudoneutid50_Beta"
## [627] "Day29pseudoneutid50_D614G"
## [628] "Day29pseudoneutid50_Delta"
## [629] "Day85pseudoneutid50_BA.1"
## [630] "Day85pseudoneutid50_BA.4.BA.5"
## [631] "Day85pseudoneutid50_Beta"
## [632] "Day85pseudoneutid50_D614G"
## [633] "Day85pseudoneutid50_Delta"
## [634] "Day91cd4_154_BA.4.5.S1"
## [635] "Day91cd4_154_BA.4.5.S1_resp"
## [636] "Day91cd4_154_BA.4.5.S2"
## [637] "Day91cd4_154_BA.4.5.S2_resp"
## [638] "Day91cd4_154_COV2.CON.S1"
## [639] "Day91cd4_154_COV2.CON.S1_resp"
## [640] "Day91cd4_154_COV2.CON.S2"
## [641] "Day91cd4_154_COV2.CON.S2_resp"
## [642] "Day91cd4_154_Wuhan.N"
## [643] "Day91cd4_154_Wuhan.N_resp"
## [644] "Day91cd4_CXCR5.154_BA.4.5.S1"
## [645] "Day91cd4_CXCR5.154_BA.4.5.S1_resp"
## [646] "Day91cd4_CXCR5.154_BA.4.5.S2"
## [647] "Day91cd4_CXCR5.154_BA.4.5.S2_resp"
## [648] "Day91cd4_CXCR5.154_COV2.CON.S1"
## [649] "Day91cd4_CXCR5.154_COV2.CON.S1_resp"
## [650] "Day91cd4_CXCR5.154_COV2.CON.S2"
## [651] "Day91cd4_CXCR5.154_COV2.CON.S2_resp"
## [652] "Day91cd4_CXCR5.154_Wuhan.N"
## [653] "Day91cd4_CXCR5.154_Wuhan.N_resp"
## [654] "Day91cd4_CXCR5.IL21_BA.4.5.S1"
## [655] "Day91cd4_CXCR5.IL21_BA.4.5.S1_resp"
## [656] "Day91cd4_CXCR5.IL21_BA.4.5.S2"
## [657] "Day91cd4_CXCR5.IL21_BA.4.5.S2_resp"
## [658] "Day91cd4_CXCR5.IL21_COV2.CON.S1"
## [659] "Day91cd4_CXCR5.IL21_COV2.CON.S1_resp"
## [660] "Day91cd4_CXCR5.IL21_COV2.CON.S2"
## [661] "Day91cd4_CXCR5.IL21_COV2.CON.S2_resp"
## [662] "Day91cd4_CXCR5.IL21_Wuhan.N"
## [663] "Day91cd4_CXCR5.IL21_Wuhan.N_resp"
## [664] "Day91cd4_IFNg_BA.4.5.S1"
## [665] "Day91cd4_IFNg_BA.4.5.S1_resp"
## [666] "Day91cd4_IFNg_BA.4.5.S2"
## [667] "Day91cd4_IFNg_BA.4.5.S2_resp"
## [668] "Day91cd4_IFNg_COV2.CON.S1"
## [669] "Day91cd4_IFNg_COV2.CON.S1_resp"
## [670] "Day91cd4_IFNg_COV2.CON.S2"
## [671] "Day91cd4_IFNg_COV2.CON.S2_resp"
## [672] "Day91cd4_IFNg_Wuhan.N"
## [673] "Day91cd4_IFNg_Wuhan.N_resp"
## [674] "Day91cd4_IFNg.IL2_BA.4.5.S1"
## [675] "Day91cd4_IFNg.IL2_BA.4.5.S1_resp"
## [676] "Day91cd4_IFNg.IL2_BA.4.5.S2"
## [677] "Day91cd4_IFNg.IL2_BA.4.5.S2_resp"
## [678] "Day91cd4_IFNg.IL2_COV2.CON.S1"
## [679] "Day91cd4_IFNg.IL2_COV2.CON.S1_resp"
## [680] "Day91cd4_IFNg.IL2_COV2.CON.S2"
## [681] "Day91cd4_IFNg.IL2_COV2.CON.S2_resp"
## [682] "Day91cd4_IFNg.IL2_Wuhan.N"
## [683] "Day91cd4_IFNg.IL2_Wuhan.N_resp"
## [684] "Day91cd4_IFNg.IL2.154_BA.4.5.S1"
## [685] "Day91cd4_IFNg.IL2.154_BA.4.5.S1_resp"
## [686] "Day91cd4_IFNg.IL2.154_BA.4.5.S2"
## [687] "Day91cd4_IFNg.IL2.154_BA.4.5.S2_resp"
## [688] "Day91cd4_IFNg.IL2.154_COV2.CON.S1"
## [689] "Day91cd4_IFNg.IL2.154_COV2.CON.S1_resp"
## [690] "Day91cd4_IFNg.IL2.154_COV2.CON.S2"
## [691] "Day91cd4_IFNg.IL2.154_COV2.CON.S2_resp"
## [692] "Day91cd4_IFNg.IL2.154_Wuhan.N"
## [693] "Day91cd4_IFNg.IL2.154_Wuhan.N_resp"
## [694] "Day91cd4_IL17a_BA.4.5.S1"
## [695] "Day91cd4_IL17a_BA.4.5.S1_resp"
## [696] "Day91cd4_IL17a_BA.4.5.S2"
## [697] "Day91cd4_IL17a_BA.4.5.S2_resp"
## [698] "Day91cd4_IL17a_COV2.CON.S1"
## [699] "Day91cd4_IL17a_COV2.CON.S1_resp"
## [700] "Day91cd4_IL17a_COV2.CON.S2"
## [701] "Day91cd4_IL17a_COV2.CON.S2_resp"
## [702] "Day91cd4_IL17a_Wuhan.N"
## [703] "Day91cd4_IL17a_Wuhan.N_resp"
## [704] "Day91cd4_IL2_BA.4.5.S1"
## [705] "Day91cd4_IL2_BA.4.5.S1_resp"
## [706] "Day91cd4_IL2_BA.4.5.S2"
## [707] "Day91cd4_IL2_BA.4.5.S2_resp"
## [708] "Day91cd4_IL2_COV2.CON.S1"
## [709] "Day91cd4_IL2_COV2.CON.S1_resp"
## [710] "Day91cd4_IL2_COV2.CON.S2"
## [711] "Day91cd4_IL2_COV2.CON.S2_resp"
## [712] "Day91cd4_IL2_Wuhan.N"
## [713] "Day91cd4_IL2_Wuhan.N_resp"
## [714] "Day91cd4_IL21_BA.4.5.S1"
## [715] "Day91cd4_IL21_BA.4.5.S1_resp"
## [716] "Day91cd4_IL21_BA.4.5.S2"
## [717] "Day91cd4_IL21_BA.4.5.S2_resp"
## [718] "Day91cd4_IL21_COV2.CON.S1"
## [719] "Day91cd4_IL21_COV2.CON.S1_resp"
## [720] "Day91cd4_IL21_COV2.CON.S2"
## [721] "Day91cd4_IL21_COV2.CON.S2_resp"
## [722] "Day91cd4_IL21_Wuhan.N"
## [723] "Day91cd4_IL21_Wuhan.N_resp"
## [724] "Day91cd4_IL4.154_BA.4.5.S1"
## [725] "Day91cd4_IL4.154_BA.4.5.S1_resp"
## [726] "Day91cd4_IL4.154_BA.4.5.S2"
## [727] "Day91cd4_IL4.154_BA.4.5.S2_resp"
## [728] "Day91cd4_IL4.154_COV2.CON.S1"
## [729] "Day91cd4_IL4.154_COV2.CON.S1_resp"
## [730] "Day91cd4_IL4.154_COV2.CON.S2"
## [731] "Day91cd4_IL4.154_COV2.CON.S2_resp"
## [732] "Day91cd4_IL4.154_Wuhan.N"
## [733] "Day91cd4_IL4.154_Wuhan.N_resp"
## [734] "Day91cd4_IL4.IL5.IL13.154_BA.4.5.S1"
## [735] "Day91cd4_IL4.IL5.IL13.154_BA.4.5.S1_resp"
## [736] "Day91cd4_IL4.IL5.IL13.154_BA.4.5.S2"
## [737] "Day91cd4_IL4.IL5.IL13.154_BA.4.5.S2_resp"
## [738] "Day91cd4_IL4.IL5.IL13.154_COV2.CON.S1"
## [739] "Day91cd4_IL4.IL5.IL13.154_COV2.CON.S1_resp"
## [740] "Day91cd4_IL4.IL5.IL13.154_COV2.CON.S2"
## [741] "Day91cd4_IL4.IL5.IL13.154_COV2.CON.S2_resp"
## [742] "Day91cd4_IL4.IL5.IL13.154_Wuhan.N"
## [743] "Day91cd4_IL4.IL5.IL13.154_Wuhan.N_resp"
## [744] "Day91cd4_IL5.IL13.154_BA.4.5.S1"
## [745] "Day91cd4_IL5.IL13.154_BA.4.5.S1_resp"
## [746] "Day91cd4_IL5.IL13.154_BA.4.5.S2"
## [747] "Day91cd4_IL5.IL13.154_BA.4.5.S2_resp"
## [748] "Day91cd4_IL5.IL13.154_COV2.CON.S1"
## [749] "Day91cd4_IL5.IL13.154_COV2.CON.S1_resp"
## [750] "Day91cd4_IL5.IL13.154_COV2.CON.S2"
## [751] "Day91cd4_IL5.IL13.154_COV2.CON.S2_resp"
## [752] "Day91cd4_IL5.IL13.154_Wuhan.N"
## [753] "Day91cd4_IL5.IL13.154_Wuhan.N_resp"
## [754] "Day91cd4_TNFa_BA.4.5.S1"
## [755] "Day91cd4_TNFa_BA.4.5.S1_resp"
## [756] "Day91cd4_TNFa_BA.4.5.S2"
## [757] "Day91cd4_TNFa_BA.4.5.S2_resp"
## [758] "Day91cd4_TNFa_COV2.CON.S1"
## [759] "Day91cd4_TNFa_COV2.CON.S1_resp"
## [760] "Day91cd4_TNFa_COV2.CON.S2"
## [761] "Day91cd4_TNFa_COV2.CON.S2_resp"
## [762] "Day91cd4_TNFa_Wuhan.N"
## [763] "Day91cd4_TNFa_Wuhan.N_resp"
## [764] "Day91cd8_IFNg_BA.4.5.S1"
## [765] "Day91cd8_IFNg_BA.4.5.S1_resp"
## [766] "Day91cd8_IFNg_BA.4.5.S2"
## [767] "Day91cd8_IFNg_BA.4.5.S2_resp"
## [768] "Day91cd8_IFNg_COV2.CON.S1"
## [769] "Day91cd8_IFNg_COV2.CON.S1_resp"
## [770] "Day91cd8_IFNg_COV2.CON.S2"
## [771] "Day91cd8_IFNg_COV2.CON.S2_resp"
## [772] "Day91cd8_IFNg_Wuhan.N"
## [773] "Day91cd8_IFNg_Wuhan.N_resp"
## [774] "Day91cd8_IFNg.IL2_BA.4.5.S1"
## [775] "Day91cd8_IFNg.IL2_BA.4.5.S1_resp"
## [776] "Day91cd8_IFNg.IL2_BA.4.5.S2"
## [777] "Day91cd8_IFNg.IL2_BA.4.5.S2_resp"
## [778] "Day91cd8_IFNg.IL2_COV2.CON.S1"
## [779] "Day91cd8_IFNg.IL2_COV2.CON.S1_resp"
## [780] "Day91cd8_IFNg.IL2_COV2.CON.S2"
## [781] "Day91cd8_IFNg.IL2_COV2.CON.S2_resp"
## [782] "Day91cd8_IFNg.IL2_Wuhan.N"
## [783] "Day91cd8_IFNg.IL2_Wuhan.N_resp"
## [784] "Day91cd8_IFNg.IL2.TNFa_BA.4.5.S1"
## [785] "Day91cd8_IFNg.IL2.TNFa_BA.4.5.S1_resp"
## [786] "Day91cd8_IFNg.IL2.TNFa_BA.4.5.S2"
## [787] "Day91cd8_IFNg.IL2.TNFa_BA.4.5.S2_resp"
## [788] "Day91cd8_IFNg.IL2.TNFa_COV2.CON.S1"
## [789] "Day91cd8_IFNg.IL2.TNFa_COV2.CON.S1_resp"
## [790] "Day91cd8_IFNg.IL2.TNFa_COV2.CON.S2"
## [791] "Day91cd8_IFNg.IL2.TNFa_COV2.CON.S2_resp"
## [792] "Day91cd8_IFNg.IL2.TNFa_Wuhan.N"
## [793] "Day91cd8_IFNg.IL2.TNFa_Wuhan.N_resp"
## [794] "Day91cd8_IL2_BA.4.5.S1"
## [795] "Day91cd8_IL2_BA.4.5.S1_resp"
## [796] "Day91cd8_IL2_BA.4.5.S2"
## [797] "Day91cd8_IL2_BA.4.5.S2_resp"
## [798] "Day91cd8_IL2_COV2.CON.S1"
## [799] "Day91cd8_IL2_COV2.CON.S1_resp"
## [800] "Day91cd8_IL2_COV2.CON.S2"
## [801] "Day91cd8_IL2_COV2.CON.S2_resp"
## [802] "Day91cd8_IL2_Wuhan.N"
## [803] "Day91cd8_IL2_Wuhan.N_resp"
## [804] "Day91cd8_TNFa_BA.4.5.S1"
## [805] "Day91cd8_TNFa_BA.4.5.S1_resp"
## [806] "Day91cd8_TNFa_BA.4.5.S2"
## [807] "Day91cd8_TNFa_BA.4.5.S2_resp"
## [808] "Day91cd8_TNFa_COV2.CON.S1"
## [809] "Day91cd8_TNFa_COV2.CON.S1_resp"
## [810] "Day91cd8_TNFa_COV2.CON.S2"
## [811] "Day91cd8_TNFa_COV2.CON.S2_resp"
## [812] "Day91cd8_TNFa_Wuhan.N"
## [813] "Day91cd8_TNFa_Wuhan.N_resp"
## [814] "Day91pseudoneutid50_BA.1"
## [815] "Day91pseudoneutid50_BA.4.BA.5"
## [816] "Day91pseudoneutid50_Beta"
## [817] "Day91pseudoneutid50_D614G"
## [818] "Day91pseudoneutid50_Delta"
## [819] "early_term"
## [820] "early_term_date"
## [821] "early_term_date_imp"
## [822] "EarlyendpointD15"
## [823] "eligibility_deviation"
## [824] "EthnicityHispanic"
## [825] "EthnicityNotreported"
## [826] "first_Npos_date"
## [827] "HSTRDPCT_SWAB1"
## [828] "HSTRDPCT_SWAB2"
## [829] "ics"
## [830] "Immunemarkerset"
## [831] "ImmunemarkersetD92toD181"
## [832] "infect_date1"
## [833] "infect_date2"
## [834] "last_contact_date"
## [835] "Multiracial"
## [836] "N_status_1.0"
## [837] "N_status_15.2"
## [838] "N_status_181.14"
## [839] "N_status_271.14"
## [840] "N_status_29.2"
## [841] "N_status_91.7"
## [842] "naive"
## [843] "NAntibody"
## [844] "NatAmer"
## [845] "NumberdaysD15toD181"
## [846] "NumberdaysD15toD29"
## [847] "NumberdaysD15toD91"
## [848] "oos_boost"
## [849] "oos_boost_date"
## [850] "oos_boost_date_imp"
## [851] "PacIsl"
## [852] "PANGOLIN_SWAB1"
## [853] "PANGOLIN_SWAB2"
## [854] "PANGOVER_SWAB1"
## [855] "PANGOVER_SWAB2"
## [856] "Perprotocol"
## [857] "ph1.D15"
## [858] "ph1.D29"
## [859] "ph1.D92"
## [860] "pre_study_booster_date"
## [861] "pre_study_booster_until_studydose1_day"
## [862] "pre_study_booster_until_studydose1_day_median"
## [863] "pre_study_booster_until_studydose1_ind"
## [864] "pre_study_infect_date"
## [865] "primary_booster_type"
## [866] "primary_vax1_date"
## [867] "primary_vax2_date"
## [868] "race"
## [869] "RESULT"
## [870] "RESULT_limit"
## [871] "SCORCALL_SWAB1"
## [872] "SCORCALL_SWAB2"
## [873] "SCORVER_SWAB1"
## [874] "SCORVER_SWAB2"
## [875] "Sex"
## [876] "SPKAADEL_SWAB1"
## [877] "SPKAADEL_SWAB2"
## [878] "SPKAAINS_SWAB1"
## [879] "SPKAAINS_SWAB2"
## [880] "SPKAASUB_SWAB1"
## [881] "SPKAASUB_SWAB2"
## [882] "stage"
## [883] "State"
## [884] "stratalab"
## [885] "studydose1date"
## [886] "studydose2date"
## [887] "Subjectid"
## [888] "symptm_infect1"
## [889] "symptm_infect1_date"
## [890] "symptm_infect1_date_imp"
## [891] "symptomatic_infect1"
## [892] "symptomatic_infect2"
## [893] "Target_study_day_numeric"
## [894] "TOTREADS_SWAB1"
## [895] "TOTREADS_SWAB2"
## [896] "treatment_actual"
## [897] "treatment_assigned"
## [898] "TrtA"
## [899] "TrtB"
## [900] "TrtC"
## [901] "TrtmRNA"
## [902] "TrtonedosemRNA"
## [903] "TrtSanofi"
## [904] "TRUNCLIN_SWAB1"
## [905] "TRUNCLIN_SWAB2"
## [906] "TRUNCVER_SWAB1"
## [907] "TRUNCVER_SWAB2"
## [908] "UNITS"
## [909] "Unknown"
## [910] "White"table(dat_mapped$treatment_actual, dat_mapped$stage)##
## 1 2 3 4
## 1 Dose Beta + Omicron (Moderna) 113 0 0 0
## 1 Dose Delta + Omicron (Moderna) 101 0 0 0
## 1 Dose Omicron (Moderna) 99 0 0 0
## 1 Dose Omicron + Prototype (Moderna) 99 0 0 0
## 1 Dose Prototype (Moderna) 99 0 0 0
## 2 Dose Beta + Omicron (Moderna) 86 0 0 0
## Beta (Pfizer 1) 0 51 0 0
## Beta (Sanofi) 0 0 51 0
## Beta + Omicron (Pfizer 1) 0 52 0 0
## Beta + Prototype (Sanofi) 0 0 52 0
## Beta + Wildtype/Prototype (Pfizer 1) 0 52 0 0
## Omicron (Pfizer 1) 0 54 0 0
## Omicron + Wildtype/Prototype (Pfizer 1) 0 53 0 0
## Omicron BA.1 + Wildtype/Prototype (Pfizer 2) 0 0 0 101
## Omicron BA.4/5 + Wildtype/Prototype (Pfizer 2) 0 0 0 100
## Prototype (Sanofi) 0 0 49 0
## Wildtype/Prototype (Pfizer 1) 0 50 0 0with(subset(dat, ph1.D15==1 & COVIDIndD22toD181), mytable(treatment_actual, COVIDlineageObserved))## COVIDlineageObserved
## treatment_actual FALSE TRUE
## 1 Dose Beta + Omicron (Moderna) 9 28
## 1 Dose Delta + Omicron (Moderna) 5 23
## 1 Dose Omicron (Moderna) 7 18
## 1 Dose Omicron + Prototype (Moderna) 8 18
## 1 Dose Prototype (Moderna) 7 20
## Beta (Pfizer 1) 4 5
## Beta + Omicron (Pfizer 1) 3 4
## Beta + Wildtype/Prototype (Pfizer 1) 2 7
## Omicron (Pfizer 1) 5 3
## Omicron + Wildtype/Prototype (Pfizer 1) 0 6
## Omicron BA.1 + Wildtype/Prototype (Pfizer 2) 3 5
## Omicron BA.4/5 + Wildtype/Prototype (Pfizer 2) 2 5
## Wildtype/Prototype (Pfizer 1) 4 12table(dat_mapped$Immunemarkerset, dat_mapped$Perprotocol, useNA='ifany')##
## 0 1
## 0 26 24
## 1 0 1212table(dat_mapped$Immunemarkerset, dat_mapped$eligibility_deviation, useNA='ifany')##
## Y
## 0 47 3
## 1 1212 0